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The idea of considering infoboxes is not novel: for example, in BIBREF7 the authors noticed that the presence of an infobox is a characteristic featured by good articles. However, in the specific case of the Medicine Portal, the presence of an infobox does not seem strictly related to the quality class the article belongs to (according to the manual labelling). Indeed, it is recurrent that articles, spanning all classes, have an infobox, containing a schematic synthesis of the article. In particular, pages with descriptions of diseases usually have an infobox with the medical standard code of the disease (i.e., IDC-9 and IDC-10), as in Figure 2 .
1603.01987
Introduction </s> As Ġobserved Ġby Ġa Ġrecent Ġarticle Ġof ĠNature ĠNews ĠB IB REF 0 Ġ, ĠâĢ ľ Wikipedia Ġis Ġamong Ġthe Ġmost Ġfrequently Ġvisited Ġwebsites Ġin Ġthe Ġworld Ġand Ġone Ġof Ġthe Ġmost Ġpopular Ġplaces Ġto Ġtap Ġinto Ġthe Ġworld 's Ġscientific Ġand Ġmedical Ġinformation ". ĠDespite Ġthe Ġhuge Ġamount Ġof Ġconsultations , Ġopen Ġissues Ġstill Ġthreaten Ġa Ġfully Ġconfident Ġfruition Ġof Ġthe Ġpopular Ġonline Ġopen Ġencyclopedia . </s> A Ġfirst Ġissue Ġrelates Ġto Ġthe Ġreliability Ġof Ġthe Ġinformation Ġavailable : Ġsince ĠWikipedia Ġcan Ġbe Ġedited Ġby Ġanyone , Ġregardless Ġof Ġtheir Ġlevel Ġof Ġexpertise , Ġthis Ġtends Ġto Ġer ode Ġthe Ġaverage Ġreputation Ġof Ġthe Ġsources , Ġand , Ġconsequently , Ġthe Ġtrust worthiness Ġof Ġthe Ġcontents Ġposted Ġby Ġthose Ġsources . ĠIn Ġan Ġattempt Ġto Ġfix Ġthis Ġshort coming , ĠWikipedia Ġhas Ġrecently Ġenlisted Ġthe Ġhelp Ġof Ġscientists Ġto Ġactively Ġsupport Ġthe Ġediting Ġon ĠWikipedia ĠB IB REF 0 Ġ. ĠFurthermore , Ġlack Ġof Ġcontrol Ġmay Ġlead Ġto Ġthe Ġpublication Ġof Ġfake ĠWikipedia Ġpages , Ġwhich Ġdistort Ġthe Ġinformation Ġby Ġinserting , Ġe . g ., Ġpromotional Ġarticles Ġand Ġpromotional Ġexternal Ġlinks . ĠFighting Ġvandalism Ġis Ġone Ġof Ġthe Ġmain Ġgoals Ġof Ġthe ĠWikimedia ĠFoundation , Ġthe Ġnonprofit Ġorganization Ġthat Ġsupports ĠWikipedia : Ġmachine Ġlearning Ġtechniques Ġhave Ġbeen Ġconsidered Ġto Ġoffer Ġa Ġservice Ġto ĠâĢ ľ jud ge Ġwhether Ġan Ġedit Ġwas Ġmade Ġin Ġgood Ġfaith Ġor Ġnot " ĠB IB REF 1 Ġ. ĠNonetheless , Ġin Ġthe Ġpast Ġrecent Ġtime , Ġmalicious Ġorganisations Ġhave Ġacted Ġdisrupt ively Ġwith Ġpurposes Ġof Ġextortion Ġ- Ġsee , Ġe . g ., Ġthe Ġrecent Ġnews Ġon Ġthe Ġuncover ing Ġof Ġa Ġblackmail Ġnetwork Ġof Ġaccounts , Ġwhich Ġthreatened Ġcelebrities Ġwith Ġthe Ġmenace Ġof Ġinserting Ġoffending Ġinformation Ġon Ġtheir ĠWikipedia Ġpages . </s> Secondly , Ġarticles Ġmay Ġsuffer Ġfrom Ġread ability Ġissues : Ġachieving Ġa Ġsynt act ical Ġaccuracy Ġthat Ġhelps Ġthe Ġreader Ġwith Ġa Ġfluid Ġreading Ġexperience Ġis ĠâĢĶ quite Ġobviously âĢĶ Ġa Ġproperty Ġwhich Ġarticles Ġshould Ġfulfill . ĠTrad itionally , Ġthe Ġliterature Ġhas Ġwidely Ġadopted Ġwell Ġknown Ġcriteria , Ġas Ġthe ĠâĢ ľ F les ch - K inc aid " Ġmeasure " ĠB IB REF 2 Ġ, Ġto Ġautomatically Ġassess Ġread ability Ġin Ġtextual Ġdocuments . ĠMore Ġrecently , Ġnew Ġtechniques Ġhave Ġbeen Ġproposed Ġtoo , Ġfor Ġassessing Ġthe Ġread ability Ġof Ġnatural Ġlanguages Ġ( see , Ġe . g ., ĠB IB REF 3 Ġfor Ġthe ĠItalian Ġuse Ġcase , ĠB IB REF 4 Ġfor Ġthe ĠSwedish Ġone , ĠB IB REF 5 Ġfor ĠEnglish ). </s> In Ġthis Ġpaper , Ġwe Ġface Ġthe Ġquest Ġfor Ġquality Ġassessment Ġof Ġa ĠWikipedia Ġarticle , Ġin Ġan Ġautomatic Ġway Ġthat Ġcomprehend s Ġnot Ġonly Ġread ability Ġand Ġreliability Ġcriteria , Ġbut Ġalso Ġadditional Ġparameters Ġtestifying Ġcomple teness Ġof Ġinformation Ġand Ġco herence Ġwith Ġthe Ġcontent Ġone Ġexpects Ġfrom Ġan Ġarticle Ġdealing Ġwith Ġspecific Ġtopics , Ġplus Ġsufficient Ġinsights Ġfor Ġthe Ġreader Ġto Ġelaborate Ġfurther Ġon Ġsome Ġargument . ĠThe Ġnotion Ġof Ġdata Ġquality Ġwe Ġdeal Ġwith Ġin Ġthe Ġpaper Ġis Ġcoherent Ġwith Ġthe Ġone Ġsuggested Ġby Ġrecent Ġcontributions Ġ( see , Ġe . g ., ĠB IB REF 6 Ġ), Ġwhich Ġpoints Ġout Ġlike Ġthe Ġquality Ġof ĠWeb Ġinformation Ġis Ġstrictly Ġconnected Ġto Ġthe Ġscope Ġfor Ġwhich Ġone Ġneeds Ġsuch Ġinformation . </s> Our Ġintuition Ġis Ġthat Ġgroups Ġof Ġarticles Ġrelated Ġto Ġa Ġspecific Ġtopic Ġand Ġfalling Ġwithin Ġspecific Ġsc opes Ġare Ġintrinsically Ġdifferent Ġfrom Ġother Ġgroups Ġon Ġdifferent Ġtopics Ġwithin Ġdifferent Ġsc opes . ĠWe Ġapproach Ġthe Ġarticle Ġevaluation Ġthrough Ġmachine Ġlearning Ġtechniques . ĠSuch Ġtechniques Ġare Ġnot Ġnew Ġto Ġbe Ġemployed Ġfor Ġautomatic Ġevaluation Ġof Ġarticles Ġquality . ĠAs Ġan Ġexample , Ġthe Ġwork Ġin ĠB IB REF 7 Ġexploits Ġclassification Ġtechniques Ġbased Ġon Ġstructural Ġand Ġlinguistic Ġfeatures Ġof Ġan Ġarticle . ĠHere , Ġwe Ġenrich Ġthat Ġmodel Ġwith Ġnovel Ġfeatures Ġthat Ġare Ġdomain - specific . ĠAs Ġa Ġrunning Ġscenario , Ġwe Ġfocus Ġon Ġthe ĠWikipedia Ġmedical Ġportal . ĠIndeed , Ġfacing Ġthe Ġproblems Ġof Ġinformation Ġquality Ġand Ġensuring Ġhigh Ġand Ġcorrect Ġlevels Ġof Ġinform at iveness Ġis Ġeven Ġmore Ġdemanding Ġwhen Ġhealth Ġaspects Ġare Ġinvolved . ĠRecent Ġstatistics Ġreport Ġthat ĠInternet Ġusers Ġare Ġincreasingly Ġsearching Ġthe ĠWeb Ġfor Ġhealth Ġinformation , Ġby Ġconsulting Ġsearch Ġengines , Ġsocial Ġnetworks , Ġand Ġspecial ised Ġhealth Ġportals , Ġlike Ġthat Ġof ĠWikipedia . ĠAs Ġpointed Ġout Ġby Ġthe Ġ2014 ĠEuro bar ometer Ġsurvey Ġon ĠEuropean Ġcitizens ' Ġdigital Ġhealth Ġliteracy , Ġaround Ġsix Ġout Ġof Ġten Ġrespondents Ġhave Ġused Ġthe ĠInternet Ġto Ġsearch Ġfor Ġhealth - related Ġinformation . ĠThis Ġmeans Ġthat , Ġalthough Ġthe Ġtrend Ġin Ġdigital Ġhealth Ġliteracy Ġis Ġgrowing , Ġthere Ġis Ġalso Ġa Ġdemand Ġfor Ġa Ġqualified Ġsource Ġwhere Ġpeople Ġcan Ġask Ġand Ġfind Ġmedical Ġinformation Ġwhich , Ġto Ġan Ġextent , Ġcan Ġprovide Ġthe Ġsame Ġlevel Ġof Ġfamiliarity Ġand Ġguarantees Ġas Ġthose Ġgiven Ġby Ġa Ġdoctor Ġor Ġa Ġhealth Ġprofessional . </s> We Ġanticipate Ġhere Ġthat Ġleveraging Ġnew Ġdomain - specific Ġfeatures Ġis Ġin Ġline Ġwith Ġthis Ġdemand Ġof Ġarticles Ġquality . ĠMoreover , Ġas Ġthe Ġoutcomes Ġof Ġour Ġexperiments Ġshow , Ġthey Ġeffectively Ġimprove Ġthe Ġclassification Ġresults Ġin Ġthe Ġhard Ġtask Ġof Ġmulti - class Ġassessment , Ġespecially Ġfor Ġthose Ġclasses Ġthat Ġother Ġautomatic Ġapproaches Ġworst Ġclassify . ĠRem ark ably , Ġour Ġproposal Ġis Ġgeneral Ġenough Ġto Ġbe Ġeasily Ġextended Ġto Ġother Ġdomains , Ġin Ġaddition Ġto Ġthe Ġmedical Ġone . </s> Section Ġ" Dat as et " Ġfirst Ġdescribes Ġthe Ġstructure Ġof Ġthe Ġarticles Ġpresent Ġin Ġthe Ġmedical Ġportal . ĠThen , Ġit Ġgives Ġdetails Ġon Ġthe Ġreal Ġdata Ġused Ġin Ġthe Ġexperiments , Ġwhich Ġare Ġindeed Ġarticles Ġextracted Ġfrom Ġthe Ġmedical Ġportal Ġand Ġlabeled Ġaccording Ġto Ġthe Ġmanual Ġassessment Ġby Ġthe ĠWikimedia Ġproject . ĠSection Ġ" Bas eline : Ġthe Ġaction able Ġmodel " Ġbriefly Ġpresents Ġthe Ġaction able Ġmodel Ġin ĠB IB REF 7 Ġ: Ġwe Ġadopt Ġit Ġas Ġthe Ġbaseline Ġfor Ġour Ġanalysis . ĠIn ĠSection Ġ" The Ġmedical Ġdomain Ġmodel " Ġ, Ġwe Ġpresent Ġthe Ġdomain - specific , Ġmedical Ġmodel Ġwe Ġnewly Ġadopt Ġin Ġthis Ġpaper Ġas Ġan Ġextension Ġof Ġthe Ġbaseline . ĠThe Ġextended Ġmodel Ġincludes Ġfeatures Ġspecifically Ġextracted Ġfrom Ġthe Ġmedical Ġdomain . ĠOne Ġnovel Ġfeature Ġis Ġbased Ġon Ġthe Ġarticle Ġtextual Ġcontent . ĠSection Ġ" Bio - medical Ġentities " Ġpresents Ġthe Ġprocess Ġwhich Ġits Ġextraction Ġrelies Ġon , Ġwith Ġa Ġnon Ġtrivial Ġanalysis Ġof Ġnatural Ġlanguage Ġand Ġdomain Ġknowledge . ĠSection Ġ" Exper iments Ġand Ġresults " Ġpresents Ġexperiments Ġand Ġresults , Ġwith Ġa Ġcomparison Ġof Ġthe Ġbaseline Ġmodel Ġwith Ġthe Ġnew Ġone . ĠIn ĠSection Ġ" Related Ġwork " Ġ, Ġwe Ġsurvey Ġrelated Ġwork Ġin Ġthe Ġarea Ġand Ġin ĠSection Ġ" Con clusions " Ġwe Ġconclude Ġthe Ġpaper . </s> Dat as et </s> We Ġconsider Ġthe Ġdataset Ġconsisting Ġof Ġthe Ġentire Ġcollection Ġof Ġarticles Ġof Ġthe ĠWikipedia ĠMedicine ĠPortal , Ġupdated Ġat Ġthe Ġend Ġof Ġ2014 . ĠWikipedia Ġarticles Ġare Ġwritten Ġaccording Ġto Ġthe ĠMedia ĠWiki Ġmarkup Ġlanguage , Ġa ĠHTML - like Ġlanguage . ĠAmong Ġthe Ġstructural Ġelements Ġof Ġone Ġpage , Ġwhich Ġdiffers Ġfrom Ġstandard ĠHTML Ġpages , Ġthere Ġare Ġi ) Ġthe Ġinternal Ġlinks , Ġi . e ., Ġlinks Ġto Ġother ĠWikipedia Ġpages , Ġdifferent Ġfrom Ġlinks Ġto Ġexternal Ġresources ); Ġii ) Ġcategories , Ġwhich Ġrepresent Ġthe ĠMedia ĠWiki Ġcategories Ġa Ġpage Ġbelongs Ġto : Ġthey Ġare Ġencoded Ġin Ġthe Ġpart Ġof Ġtext Ġwithin Ġthe ĠMedia ĠWiki ĠâĢ ľ c ategories " Ġtag Ġin Ġthe Ġpage Ġsource , Ġand Ġiii ) Ġinformative Ġboxes , Ġso Ġcalled ĠâĢ ľ inf ob ox es ", Ġwhich Ġsummarize Ġin Ġa Ġstructured Ġmanner Ġsome Ġpeculiar Ġpieces Ġof Ġinformation Ġrelated Ġthe Ġtopic Ġof Ġthe Ġarticle . ĠThe Ġcategory Ġvalues Ġfor Ġthe Ġarticles Ġin Ġthe Ġmedical Ġportal Ġspan Ġover Ġthe Ġones Ġlisted Ġat Ġhttps :// en . wikipedia . org / wiki / Port al : Medic ine . ĠExamples Ġof Ġcategories , Ġwhich Ġappear Ġat Ġthe Ġbottom Ġof Ġeach ĠWikipedia Ġpage , Ġare Ġin ĠFig . Ġ1 Ġ. </s> Inf ob ox es Ġof Ġthe Ġmedical Ġportal Ġfeature Ġmedical Ġcontent Ġand Ġstandard Ġcoding . ĠAs Ġan Ġexample , ĠFig . Ġ2 Ġshows Ġthe Ġinf ob ox Ġin Ġthe ĠAlzheimer 's Ġdisease Ġpage Ġof Ġthe Ġportal . ĠThe Ġinf ob ox Ġcontains Ġexplanatory Ġfigures Ġand Ġtext Ġden oting Ġpeculiar Ġcharacteristics Ġof Ġthe Ġdisease Ġand Ġthe Ġvalue Ġfor Ġthe Ġstandard Ġcode Ġof Ġsuch Ġdisease Ġ( IC D 9 , Ġas Ġfor Ġthe Ġinternational Ġclassification Ġof Ġthe Ġdisease ). </s> Thanks Ġto ĠWiki Project ĠMedicine , Ġthe Ġdataset Ġof Ġarticles Ġwe Ġcollected Ġfrom Ġthe ĠWikipedia ĠMedicine ĠPortal Ġhas Ġbeen Ġmanually Ġlabeled Ġinto Ġseven Ġquality Ġclasses . ĠThey Ġare Ġordered Ġas ĠStub , ĠStart , ĠC , ĠB , ĠA , ĠGood ĠArticle Ġ( GA ), ĠFeatured ĠArticle Ġ( FA ). ĠThe ĠFeatured Ġand ĠGood Ġarticle Ġclasses Ġare Ġthe Ġhighest Ġones : Ġto Ġhave Ġthose Ġlabels , Ġan Ġarticle Ġrequires Ġa Ġcommunity Ġconsensus Ġand Ġan Ġofficial Ġreview Ġby Ġselected Ġeditors , Ġwhile Ġthe Ġother Ġlabels Ġcan Ġbe Ġachieved Ġwith Ġreviews Ġfrom Ġa Ġlarger , Ġeven Ġcontrolled , Ġset Ġof Ġeditors . ĠActually , Ġnone Ġof Ġthe Ġarticles Ġin Ġthe Ġdataset Ġis Ġlabeled Ġas ĠA , Ġthus , Ġin Ġthe Ġfollowing , Ġwe Ġdo Ġnot Ġconsider Ġthat Ġclass , Ġrestricting Ġthe Ġinvestigation Ġto Ġsix Ġclasses . </s> At Ġthe Ġdate Ġof Ġour Ġstudy , Ġwe Ġwere Ġable Ġto Ġgather Ġ24 , 362 Ġrated Ġdocuments . ĠRem ark ably , Ġonly Ġa Ġsmall Ġpercentage Ġof Ġthem Ġ( 1 %) Ġis Ġlabeled Ġas ĠGA Ġand ĠFA . ĠIndeed , Ġthe Ġdistribution Ġof Ġthe Ġarticles Ġamong Ġthe Ġclasses Ġis Ġhighly Ġskewed . ĠThere Ġare Ġvery Ġfew Ġ( 201 ) Ġarticles Ġfor Ġthe Ġhighest Ġquality Ġclasses Ġ( FA Ġand ĠGA ), Ġwhile Ġthe Ġvast Ġmajority Ġ( 19 , 108 ) Ġbelongs Ġto Ġthe Ġlowest Ġquality Ġones Ġ( St ub Ġand ĠStart ). ĠThis Ġholds Ġnot Ġonly Ġfor Ġthe Ġmedical Ġportal . ĠIndeed , Ġit Ġis Ġcommon Ġin Ġall ĠWikipedia , Ġwhere , Ġon Ġaverage , Ġonly Ġone Ġarticle Ġin Ġevery Ġthousand Ġis Ġa ĠFeatured Ġone . </s> In ĠSection Ġ" Exper iments Ġand Ġresults " Ġ, Ġwe Ġwill Ġadopt Ġa Ġset Ġof Ġmachine - learning Ġclass ifiers Ġto Ġautomatically Ġlabel Ġthe Ġarticles Ġinto Ġthe Ġquality Ġclasses . ĠDe aling Ġwith Ġim balanced Ġclasses Ġis Ġa Ġcommon Ġsituation Ġin Ġmany Ġreal Ġapplications Ġof Ġclassification Ġlearning : Ġhealthy Ġpatients Ġover Ġthe Ġpopulation , Ġfraudulent Ġactions Ġover Ġdaily Ġgenuine Ġtransactions , Ġand Ġso Ġon . ĠWithout Ġany Ġcounter me asure , Ġcommon Ġclass ifiers Ġtend Ġto Ġcorrectly Ġidentify Ġonly Ġarticles Ġbelonging Ġto Ġthe Ġmajority Ġclasses , Ġclearly Ġleading Ġto Ġsevere Ġmis - class ification Ġof Ġthe Ġminority Ġclasses , Ġsince Ġtypical Ġlearning Ġalgorithms Ġstrive Ġto Ġmaximize Ġthe Ġoverall Ġprediction Ġaccuracy . ĠTo Ġreduce Ġthe Ġdise qu ilibrium Ġamong Ġthe Ġsize Ġof Ġthe Ġclasses , Ġwe Ġhave Ġfirst Ġrandomly Ġsampled Ġthe Ġarticles Ġbelonging Ġto Ġthe Ġmost Ġpopulated Ġclasses . ĠThen , Ġwe Ġhave Ġperformed Ġsome Ġfurther Ġelabor ation , Ġas Ġshown Ġin Ġthe Ġfollowing . </s> Many Ġstudies Ġhave Ġbeen Ġconducted Ġto Ġimprove Ġlearning Ġalgorithms Ġaccuracy Ġin Ġpresence Ġof Ġim balanced Ġdata ĠB IB REF 8 Ġ. ĠFor Ġthe Ġcurrent Ġwork , Ġwe Ġhave Ġconsidered Ġone Ġof Ġthe Ġmost Ġpopular Ġapproaches , Ġnamely Ġthe ĠSynt hetic ĠSam pling Ġwith ĠData ĠGeneration , Ġdetailed Ġin ĠB IB REF 9 Ġ. ĠIt Ġconsists Ġin Ġgenerating Ġsynthetic Ġinstances Ġfrom Ġthe Ġminority Ġclasses , Ġto Ġbalance Ġthe Ġoverall Ġdataset . ĠThe Ġapproach Ġhas Ġbeen Ġbroadly Ġapplied Ġto Ġproblems Ġrelying Ġon ĠN LP Ġfeatures , Ġsee , Ġe . g ., ĠB IB REF 10 Ġ. ĠIn Ġour Ġcase , Ġwe Ġres am pled Ġthe Ġinput Ġdata Ġset Ġby Ġapplying Ġthe ĠSynt hetic ĠMinority ĠOvers am pling ĠTE chn ique Ġ( SM OTE ), Ġwith Ġpercentage Ġ40 % Ġfor ĠGA Ġand Ġ180 %, Ġfor ĠFA . ĠIn Ġparticular , Ġthe Ġsteps Ġto Ġovers ample Ġare Ġthe Ġfollowing : </s> Table Ġ1 Ġshows Ġthe Ġnumber Ġof Ġarticles Ġin Ġthe Ġdataset , Ġdivided Ġper Ġclass , Ġas Ġwell Ġas Ġthe Ġrandom Ġsamples Ġwe Ġhave Ġconsidered Ġfor Ġour Ġstudy . ĠThe Ġexperiments Ġpresented Ġin ĠSection Ġ" Exper iments Ġand Ġresults " Ġare Ġbased Ġon Ġthe Ġarticles Ġof Ġthe Ġright - hand Ġcolumn Ġin Ġthe Ġtable . </s> Bas eline : Ġthe Ġaction able Ġmodel </s> We Ġapply Ġa Ġmulti - class Ġclassification Ġapproach Ġto Ġlabel Ġthe Ġarticles Ġof Ġthe Ġsampled Ġdataset Ġinto Ġthe Ġsix ĠWiki Project Ġquality Ġclasses . ĠIn Ġorder Ġto Ġhave Ġa Ġbaseline , Ġwe Ġfirst Ġapply Ġthe Ġstate Ġof Ġthe Ġart Ġmodel Ġproposed Ġin ĠB IB REF 7 Ġto Ġthe Ġdataset . </s> The ĠâĢ ľ action able Ġmodel " Ġin ĠB IB REF 7 Ġfocuses Ġon Ġfive Ġlinguistic Ġand Ġstructural Ġfeatures Ġand Ġit Ġweighs Ġthem Ġas Ġfollows : </s> where </s> In Ġorder Ġto Ġevaluate Ġsuch Ġmodel Ġover Ġour Ġdataset , Ġwe Ġhave Ġextracted Ġfrom Ġour Ġthe Ġdataset Ġthe Ġabove Ġmentioned Ġfeatures . ĠWe Ġhave Ġmeasured ĠArticle Length , ĠNum Wik il inks Ġand ĠNum Bro ken Wik il inks Ġas Ġsuggested Ġin ĠB IB REF 11 Ġ. </s> As Ġshown Ġin ĠFigure Ġ3 Ġ, Ġthe Ġaction able Ġmodel Ġfeatures Ġhave Ġbeen Ġextracted Ġapplying Ġsimple Ġscripts Ġmainly Ġbased Ġon Ġregular Ġexpressions Ġ( the ĠâĢ ľ re gex p Ġextract or " Ġblock ), Ġwhich Ġprocess Ġthe Ġwhole ĠHTML Ġcode Ġof Ġan Ġarticle Ġ( free Ġtext Ġplus Ġmedia Ġwiki Ġtags ). ĠThe Ġregex p Ġextract or Ġrelies Ġon ĠPython ĠBeautiful S oup Ġfor Ġextracting ĠHTML Ġstructures Ġand Ġexcerpts Ġof Ġthe Ġtextual Ġcontent Ġwithin Ġthe ĠMedia Wiki Ġtags Ġand Ġon Ġn lt k Ġlibraries Ġfor Ġbasic ĠN LP Ġanalysis . ĠIn Ġdetails , Ġn lt k Ġhas Ġbeen Ġused Ġfor Ġcomputing Ġthe ĠInfo No ise Ġfeature , Ġwhose Ġcomputation Ġincludes Ġthe Ġstop words Ġremoval , Ġfollowing Ġthe ĠPorter ĠStop words ĠCorpus Ġavailable Ġthrough Ġn lt k ĠB IB REF 12 Ġ. </s> The Ġclassification Ġresults Ġaccording Ġto Ġthe Ġbaseline Ġmodel Ġare Ġreported Ġin ĠSection Ġ" Exper iments Ġand Ġresults " Ġ. </s> The Ġmedical Ġdomain Ġmodel </s> Here , Ġwe Ġimprove Ġthe Ġbaseline Ġmodel Ġwith Ġnovel Ġand Ġspecifically Ġcrafted Ġfeatures Ġthat Ġrely Ġon Ġthe Ġmedical Ġdomain Ġand Ġthat Ġcapture Ġdetails Ġon Ġthe Ġspecific Ġcontent Ġof Ġan Ġarticle . ĠAs Ġshown Ġin ĠFigure Ġ3 Ġ, Ġmedical Ġmodel Ġfeatures , Ġthe Ġbio - medical Ġentities , Ġhave Ġbeen Ġextracted Ġfrom Ġthe Ġfree Ġtext Ġonly , Ġexploiting Ġadvanced ĠN LP Ġtechniques Ġand Ġusing Ġdomain Ġdiction aries . </s> In Ġdetails , Ġwe Ġnewly Ġdefine Ġand Ġextract Ġfrom Ġthe Ġdataset Ġthe Ġfollowing Ġnovel Ġfeatures : </s> The Ġidea Ġof Ġconsidering Ġinf ob ox es Ġis Ġnot Ġnovel : Ġfor Ġexample , Ġin ĠB IB REF 7 Ġthe Ġauthors Ġnoticed Ġthat Ġthe Ġpresence Ġof Ġan Ġinf ob ox Ġis Ġa Ġcharacteristic Ġfeatured Ġby Ġgood Ġarticles . ĠHowever , Ġin Ġthe Ġspecific Ġcase Ġof Ġthe ĠMedicine ĠPortal , Ġthe Ġpresence Ġof Ġan Ġinf ob ox Ġdoes Ġnot Ġseem Ġstrictly Ġrelated Ġto Ġthe Ġquality Ġclass Ġthe Ġarticle Ġbelongs Ġto Ġ( according Ġto Ġthe Ġmanual Ġlab elling ). ĠIndeed , Ġit Ġis Ġrecurrent Ġthat Ġarticles , Ġspanning Ġall Ġclasses , Ġhave Ġan Ġinf ob ox , Ġcontaining Ġa Ġschematic Ġsynthesis Ġof Ġthe Ġarticle . ĠIn Ġparticular , Ġpages Ġwith Ġdescriptions Ġof Ġdiseases Ġusually Ġhave Ġan Ġinf ob ox Ġwith Ġthe Ġmedical Ġstandard Ġcode Ġof Ġthe Ġdisease Ġ( i . e ., ĠID C - 9 Ġand ĠID C - 10 ), Ġas Ġin ĠFigure Ġ2 Ġ. </s> As Ġdone Ġfor Ġthe Ġbaseline , Ġalso Ġthe Ġfirst Ġtwo Ġfeatures Ġof Ġthe Ġmedical Ġmodel Ġhave Ġbeen Ġextracted Ġwith Ġad Ġhoc ĠPython Ġscripts , Ġextracting ĠHTML Ġstructures Ġand Ġexcerpts Ġof Ġthe Ġtextual Ġcontent Ġwithin Ġthe ĠMedia Wiki Ġtags . ĠFor Ġtheir Ġextraction Ġof Ġthe Ġbio - medical Ġentities , Ġwe Ġconsider Ġthe Ġtextual Ġpart Ġof Ġthe Ġarticle Ġonly , Ġobtained Ġafter Ġremoving Ġthe ĠMedia Wiki Ġtags , Ġand Ġwe Ġapply Ġa ĠN LP Ġanalysis , Ġwhich Ġis Ġpresented Ġin ĠSection Ġ" Bio - medical Ġentities " Ġ. </s> Inf ob ox - based Ġfeature </s> We Ġhave Ġcalculated Ġthe ĠInf ob ox Ġsize Ġas Ġthe Ġbase Ġ10 Ġlog Ġof Ġthe Ġbytes Ġof Ġdata Ġcontained Ġwithin Ġthe Ġmedia wiki Ġtags Ġthat Ġwrap Ġan Ġinf ob ox , Ġand Ġwe Ġhave Ġnormalized Ġit Ġwith Ġrespect Ġto Ġthe ĠArticle Length , Ġintroduced Ġin ĠSection Ġ" Bas eline : Ġthe Ġaction able Ġmodel " Ġ. </s> Category - based Ġfeature </s> We Ġhave Ġlever aged Ġthe Ġcategories Ġassigned Ġto Ġarticles Ġin ĠWikipedia , Ġin Ġparticular Ġrelating Ġto Ġthe Ġmedicine Ġtopics Ġavailable Ġat Ġhttps :// en . wikipedia . org / wiki / Port al : Medic ine . </s> We Ġhave Ġdefined Ġ4 Ġupper Ġlevel Ġcategories Ġof Ġour Ġinterest : </s> A Ġanatomy : Ġan Ġarticle Ġis Ġabout Ġanatomy ; </s> B Ġbiography : Ġan Ġarticle Ġis Ġa Ġbiography Ġof Ġsomeone Ġor Ġtell Ġthe Ġhistory Ġof Ġsomething ; </s> D Ġdisorder : Ġit Ġis Ġabout Ġa Ġdisorder ; </s> F Ġfirst Ġaid : Ġit Ġreports Ġinformation Ġfor Ġfirst Ġaid Ġor Ġemergency Ġcontacts ; </s> O Ġother : Ġnone Ġof Ġthe Ġabove . </s> We Ġhave Ġmatched Ġthe Ġarticle 's Ġtext Ġwithin Ġthe ĠMedia Wiki Ġcategories Ġtag Ġwith Ġan Ġapproximate Ġlist Ġof Ġkeywords Ġthat Ġare Ġrelated Ġto Ġour Ġcategory Ġof Ġinterest , Ġas Ġreported Ġin ĠTable Ġ2 Ġ. </s> Bio - medical Ġentities </s> In Ġthe Ġliterature , Ġthere Ġare Ġseveral Ġmethods Ġavailable Ġfor Ġextracting Ġbio - medical Ġentities Ġfrom Ġa Ġtext Ġ( i . e ., Ġfrom Ġmedical Ġnotes Ġand / or Ġarticles ). ĠWe Ġrefer Ġto ĠB IB REF 13 Ġfor Ġan Ġoverview Ġof Ġvaluable Ġexisting Ġtechniques . ĠIn Ġthis Ġwork , Ġwe Ġhave Ġadopted Ġa Ġdictionary - based Ġapproach , Ġwhich Ġexploits Ġlex ical Ġfeatures Ġand Ġdomain Ġknowledge Ġextracted Ġfrom Ġthe ĠUnified ĠMedical ĠLanguages ĠSystem Ġ( U ML S ) ĠMet at hes aurus ĠB IB REF 14 Ġ. ĠThe Ġapproach Ġhas Ġbeen Ġproposed Ġfor Ġthe ĠItalian Ġlanguage Ġin Ġa Ġpast Ġwork ĠB IB REF 15 Ġ. ĠSince Ġthe Ġapproach Ġcombines Ġthe Ġusage Ġof Ġlinguistic Ġanalysis Ġand Ġdomain Ġresources , Ġwe Ġwere Ġable Ġto Ġconveniently Ġadapt Ġit Ġfor Ġthe ĠEnglish Ġlanguage , Ġbeing Ġboth Ġthe Ġlinguistic Ġpipeline Ġand ĠU ML S Ġavailable Ġfor Ġmultiple Ġlanguages Ġ( including ĠEnglish Ġand ĠItalian ). </s> D ictionary - based Ġapproaches Ġhave Ġbeen Ġproved Ġvalid Ġfor Ġthe Ġtask Ġof Ġentities ' Ġextraction , Ġsee , Ġfor Ġexample , Ġanother Ġwell Ġknown , Ġsimilar Ġapproach Ġto Ġthe Ġone Ġadopted Ġhere , Ġi . e ., ĠMet am ap . ĠIt Ġis Ġworth Ġnoting Ġlike , Ġeven Ġthough Ġdictionary - based Ġapproaches Ġcould Ġbe Ġless Ġprecise Ġthan ĠNamed ĠEntity ĠRecogn ition ĠB IB REF 13 Ġ, Ġin Ġour Ġcontext Ġeven Ġan Ġapproximate Ġsolution Ġis Ġenough , Ġsince Ġwe Ġare Ġnot Ġannot ating Ġmedical Ġrecords . ĠInstead , Ġwe Ġare Ġquant ifying Ġthe Ġmole Ġof Ġinherent Ġinformation Ġwithin Ġa Ġtext . </s> Reference Ġdictionary </s> Several Ġont ologies Ġor Ġtax onom ies Ġrelated Ġto Ġthe Ġmedical Ġdomain Ġare Ġavailable Ġin ĠEnglish . ĠTo Ġbuild Ġa Ġmedical Ġdictionary , Ġwe Ġhave Ġextracted Ġdefinitions Ġof Ġmedical Ġentities Ġfrom Ġthe ĠUnified ĠMedical ĠLanguages ĠSystem Ġ( U ML S ) ĠMet at hes aurus ĠB IB REF 14 Ġ. ĠU ML S Ġintegrates Ġbio - medical Ġresources , Ġsuch Ġas ĠSN OM ED - CT Ġthat Ġprovides Ġthe Ġcore Ġterminology Ġfor Ġelectronic Ġhealth Ġrecords . ĠIn Ġaddition , ĠU ML S Ġalso Ġprovides Ġa Ġsemantic Ġnetwork Ġwhere Ġeach Ġentity Ġin Ġthe ĠMet at hes aurus Ġhas Ġan Ġassigned ĠConcept ĠUnique ĠIdent ifier Ġ( C UI ) Ġand Ġit Ġis Ġsem antically Ġtyped . </s> From ĠU ML S , Ġwe Ġhave Ġextracted Ġthe Ġentries Ġbelonging Ġto Ġthe Ġfollowing ĠSN OM ED - CT Ġsemantic Ġgroups : ĠTreatment , ĠSign Ġor ĠSym pt om , ĠDisease Ġor ĠSyndrome , ĠBody ĠParts , ĠOr gans , Ġor ĠOrgan ĠComponents , ĠPath ologic ĠFunction , Ġand ĠMental Ġor ĠBehavioral ĠDys function , Ġfor Ġa Ġtotal Ġof Ġmore Ġthan Ġone Ġmillion Ġentries , Ġas Ġshown Ġin ĠTable Ġ3 Ġ( where Ġthe Ġtwo Ġlast Ġsemantic Ġgroups Ġhave Ġbeen Ġgrouped Ġtogether , Ġunder ĠDisorder ). ĠFurthermore , Ġwe Ġhave Ġextracted Ġcommon ĠDrugs Ġand ĠActive ĠIngredients Ġdefinitions Ġfrom ĠRx Norm , Ġaccessed Ġby ĠRx Term . </s> Starting Ġfrom Ġthe Ġentries Ġin ĠTable Ġ3 Ġ, Ġwe Ġhave Ġalso Ġcomputed Ġapproximate Ġdefinitions , Ġexploiting Ġsynt actic Ġinformation Ġof Ġthe Ġsame Ġentries . ĠIn Ġdetails , Ġwe Ġhave Ġpre - process ed Ġthe Ġentries Ġby Ġmean Ġof Ġthe ĠTan l Ġpipeline ĠB IB REF 16 Ġ, Ġa Ġsuite Ġof Ġmodules Ġfor Ġtext Ġanalytics Ġand ĠN LP , Ġbased Ġon Ġmachine Ġlearning . ĠPre processing Ġhas Ġconsisted Ġin Ġfirst Ġdividing Ġthe Ġentries Ġinto Ġsingle Ġword Ġforms . ĠThen , Ġfor Ġeach Ġform , Ġwe Ġhave Ġidentified Ġthe Ġle mma Ġ( when Ġavailable ) Ġand Ġthe Ġpart Ġof Ġspeech Ġ( POS ). ĠThus , Ġwe Ġhave Ġcreated Ġan Ġapproximate Ġdefinition Ġthat Ġconsists Ġin Ġusing Ġonly Ġthe Ġle mma Ġand Ġcleaning Ġthe Ġtext , Ġexcluding Ġpunct uation , Ġpre pos itions Ġand Ġarticles . ĠAlso , Ġapproximate Ġdefinitions Ġhave Ġbeen Ġnormalized Ġby Ġlower c asing Ġeach Ġword . ĠAs Ġan Ġexample , Ġthe ĠDisorder Ġentry ĠâĢ ľ ane ur ys m Ġof Ġthe Ġvein Ġof Ġgal en " Ġhas Ġbeen Ġstored Ġin Ġthe Ġdictionary , Ġalong Ġwith Ġits Ġapproximate Ġdefinition ĠâĢ ľ ane ur ys m Ġvein Ġgal en ". </s> Ext raction Ġof Ġbio - medical Ġentities </s> We Ġhave Ġextracted Ġthe Ġbio - medical Ġentities Ġpresent Ġin Ġthe ĠWikipedia Ġmedical Ġarticles Ġthrough Ġa Ġn - gram - based Ġtechnique . </s> A Ġpre - processing Ġphase Ġoccurs Ġin Ġa Ġsimilar Ġway Ġas Ġfor Ġthe Ġdictionary Ġcomposition . ĠGiven Ġa ĠWikipedia Ġarticle Ġwritten Ġin ĠEnglish , Ġwe Ġhave Ġpre - process ed Ġthe Ġtextual Ġpart Ġthrough Ġthe ĠTan l Ġpipeline . ĠSimilar Ġto Ġwhat Ġdescribed Ġin ĠSection Ġ" Reference Ġdictionary " Ġfor Ġthe Ġreference Ġdictionary , Ġwe Ġhave Ġfirst Ġdivided Ġthe Ġtext Ġin Ġsentences Ġand Ġthe Ġsentences Ġinto Ġsingle Ġword Ġforms . ĠFor Ġeach Ġform , Ġwe Ġhave Ġconsidered Ġthe Ġle mma Ġ( when Ġavailable ) Ġand Ġthe Ġpart Ġof Ġspeech Ġ( POS ). ĠFor Ġinstance , Ġstarting Ġfrom Ġan Ġexample Ġsentence Ġextracted Ġfrom Ġthe ĠWikipedia Ġpage Ġon Ġthe ĠAlzheimer 's Ġdisease : ĠâĢ ľ Other Ġrisk Ġfactors Ġinclude Ġa Ġhistory Ġof Ġhead Ġinjuries , Ġdepression , Ġor Ġhypertension .", Ġwe Ġhave Ġobtained Ġthe Ġannotation Ġshown Ġin ĠFigure Ġ4 Ġ. </s> As Ġin Ġthe Ġcase Ġof Ġthe Ġdictionary , Ġeach Ġword Ġin Ġthe Ġtext Ġhas Ġbeen Ġlower c asing . </s> After Ġpre - processing Ġthe Ġtext Ġof Ġeach Ġarticle , Ġwe Ġhave Ġattempted Ġto Ġmatch Ġeach Ġn - gram Ġ( with Ġn Ġbetween Ġ1 Ġand Ġ10 ) Ġin Ġthe Ġcorpus Ġwith Ġthe Ġentries Ġin Ġthe Ġextended Ġdictionary . ĠWe Ġboth Ġattempt Ġan Ġexact Ġmatch Ġand Ġan Ġapproximate Ġmatch , Ġthe Ġlatter Ġremoving Ġpre pos itions , Ġpunct uations Ġand Ġarticles Ġfrom Ġthe Ġn - gram s . ĠApp rox imate Ġmatching Ġleads Ġto Ġseveral Ġadvantages . ĠIndeed , Ġexploiting Ġthe Ġtext Ġpre - processing Ġallows Ġto Ġidentify Ġdictionary Ġdefinitions Ġpresent Ġin Ġthe Ġtext , Ġeven Ġwhen Ġthe Ġnumber Ġdiffers . ĠAs Ġan Ġexample , Ġthe Ġdictionary Ġdefinition ĠâĢ ľ in jury " Ġwill Ġmatch Ġwith ĠâĢ ľ in juries ", Ġmentioned Ġin Ġthe Ġtext , Ġbecause Ġin Ġthe Ġapproximation Ġone Ġcan Ġconsider Ġthe Ġle mm as . ĠFurther , Ġconsidering Ġthe ĠPOS Ġallows Ġto Ġidentify Ġmentions Ġwhen Ġinter le aved Ġby Ġpre pos itions , Ġarticles , Ġand Ġconj unctions Ġthat Ġchange Ġthe Ġform Ġbut Ġdo Ġnot Ġalter Ġthe Ġmeaning . ĠAs Ġan Ġexample , Ġthe Ġapproximate Ġdefinition ĠâĢ ľ ane ur ys m Ġvein Ġgal en âĢ Ŀ Ġwill Ġmatch Ġalso Ġwith Ġthe Ġfollowing Ġn - gram : ĠâĢ ľ the Ġan eur ys m Ġand Ġvein Ġof Ġgal en ", Ġif Ġpresent Ġin Ġthe Ġtext . </s> Exper iments Ġand Ġresults </s> In Ġthis Ġsection , Ġwe Ġdescribe Ġthe Ġexperiments Ġand Ġreport Ġthe Ġresults Ġfor Ġthe Ġclassification Ġof ĠWikipedia Ġmedical Ġarticles Ġinto Ġthe Ġsix Ġclasses Ġof Ġthe ĠWikipedia ĠMedicine ĠPortal . ĠWe Ġcompare Ġthe Ġresults Ġobtained Ġadopting Ġfour Ġdifferent Ġclass ifiers : Ġthe Ġaction able Ġmodel Ġin ĠB IB REF 7 Ġand Ġthree Ġclass ifiers Ġthat Ġleverage Ġthe Ġad - h oc Ġfeatures Ġfrom Ġthe Ġmedical Ġdomain Ġdiscussed Ġin Ġthe Ġprevious Ġsections . ĠAll Ġthe Ġexperiments Ġwere Ġrealized Ġwithin Ġthe ĠWe ka Ġframework ĠB IB REF 17 Ġand Ġvalidated Ġthrough Ġ10 Ġfold Ġcross - valid ation . </s> For Ġeach Ġexperiment , Ġwe Ġrelied Ġon Ġthe Ġdataset Ġpresented Ġin ĠSection Ġ" Dat as et " Ġ, Ġand Ġspecifically , Ġon Ġthat Ġobtained Ġafter Ġsampling Ġthe Ġmajority Ġclasses Ġand Ġovers am pling Ġthe Ġminority Ġones Ġ( right - hand Ġcolumn Ġin ĠTable Ġ1 Ġ). ĠThe Ġdataset Ġserves Ġboth Ġas Ġtraining Ġand Ġtest Ġset Ġfor Ġthe Ġclass ifiers . </s> Moreover , Ġto Ġtake Ġinto Ġaccount Ġthe Ġim balanced Ġdata , Ġwe Ġhave Ġapplied Ġseveral Ġclassification Ġalgorithms Ġand , Ġfor Ġthe Ġsake Ġof Ġconc is eness , Ġhereafter Ġwe Ġreport Ġonly Ġthe Ġbest Ġresults Ġwe Ġhave Ġachieved . ĠIn Ġparticular , Ġwe Ġhave Ġexperimented Ġwith Ġbag ging , Ġadaptive Ġboosting Ġand Ġrandom Ġforest Ġand Ġwe Ġreport Ġthe Ġresults Ġfor Ġthe Ġlatter Ġonly . </s> Class ifiers ' Ġfeatures </s> In ĠTable Ġ4 Ġ, Ġwe Ġreport Ġa Ġsummary Ġof Ġthe Ġfeatures Ġfor Ġeach Ġof Ġthe Ġconsidered Ġmodels : Ġthe Ġbaseline Ġmodel Ġin ĠB IB REF 7 Ġand Ġtwo Ġnew Ġmodels Ġthat Ġemploy Ġthe Ġmedical Ġdomain Ġfeatures . ĠIn Ġthe ĠMedical ĠDomain Ġmodel , Ġwe Ġadd Ġto Ġthe Ġbaseline Ġfeatures Ġthe ĠDomain ĠIn format iveness , Ġas Ġdescribed Ġin ĠSection Ġ" The Ġmedical Ġdomain Ġmodel " Ġand Ġ" Bio - medical Ġentities " Ġ. ĠIn Ġaddition , Ġthe ĠFull ĠMedical ĠDomain Ġmodel Ġalso Ġconsiders Ġthe Ġfeatures ĠInfo Box Norm Size Ġand ĠCategory . </s> For Ġeach Ġof Ġthe Ġfeatures , Ġthe Ġtable Ġalso Ġreports Ġthe ĠInformation ĠGain , Ġevaluated Ġon Ġthe Ġwhole Ġdataset Ġ( 24 , 362 Ġarticles ). ĠInformation ĠGain Ġis Ġa Ġwell - known Ġmetric Ġto Ġevaluate Ġthe Ġdependency Ġof Ġone Ġclass Ġfrom Ġa Ġsingle Ġfeature , Ġsee , Ġe . g ., ĠB IB REF 18 Ġ. </s> We Ġcan Ġobserve Ġhow Ġthe ĠDomain ĠIn format iveness Ġfeature Ġhas Ġa Ġconsiderably Ġhigher Ġinf og ain Ġvalue Ġwhen Ġcompared Ġwith ĠIn format iveness . ĠWe Ġanticipate Ġhere Ġthat Ġthis Ġwill Ġlead Ġto Ġa Ġmore Ġaccurate Ġclassification Ġresults Ġfor Ġthe Ġhighest Ġclasses , Ġas Ġreported Ġin Ġthe Ġnext Ġsection . ĠLeading Ġto Ġa Ġgreater Ġaccuracy Ġis Ġalso Ġtrue Ġfor Ġthe Ġother Ġtwo Ġnew Ġfeatures Ġthat , Ġdespite Ġshowing Ġlower Ġvalues Ġof Ġinf og ain , Ġare Ġable Ġto Ġfurther Ġimprove Ġthe Ġclassification Ġresults , Ġmainly Ġfor Ġthe Ġarticles Ġbelonging Ġto Ġthe Ġlowest Ġquality Ġclasses Ġ( St ub Ġand ĠStart ). </s> Class ification Ġresults </s> Table Ġ5 Ġshows Ġthe Ġresults Ġof Ġour Ġmulti - class Ġclassification . ĠFor Ġeach Ġof Ġthe Ġclasses , Ġwe Ġhave Ġcomputed Ġthe ĠR OC ĠArea Ġand ĠF - Measure Ġmetrics ĠB IB REF 19 Ġ. ĠThe Ġlatter , Ġin Ġparticular , Ġis Ġusually Ġconsidered Ġa Ġsignificant Ġmetric Ġin Ġterms Ġof Ġclassification , Ġsince Ġit Ġcombines Ġin Ġone Ġsingle Ġvalue Ġall Ġthe Ġfour Ġindicators Ġthat Ġare Ġgenerally Ġimplied Ġfor Ġevaluating Ġthe Ġclass ifier Ġperformance Ġ( i . e ., Ġnumber Ġof ĠTrue ĠP osit ives , ĠFalse ĠP osit ives , ĠTrue ĠNeg atives Ġand ĠFalse ĠNeg atives ). ĠIn Ġour Ġscenario , Ġthe Ġmeaning Ġof Ġthe Ġindicators , Ġfor Ġeach Ġclass , Ġare Ġas Ġfollows : </s> True ĠP osit ives Ġare Ġthe Ġarticles Ġclassified Ġas Ġbelonging Ġto Ġa Ġcertain Ġclass , Ġthat Ġindeed Ġbelong Ġto Ġthat Ġclass Ġ( according Ġto Ġthe Ġquality Ġratings Ġgiven Ġby Ġthe ĠWiki Project ĠMedicine ); </s> True ĠNeg atives Ġare Ġthe Ġarticles Ġclassified Ġas Ġnot Ġbelonging Ġto Ġa Ġcertain Ġclass , Ġthat Ġindeed Ġdo Ġnot Ġbelong Ġto Ġthat Ġclass ; </s> False ĠP osit ives Ġare Ġthe Ġarticles Ġclassified Ġas Ġbelonging Ġto Ġa Ġcertain Ġclass , Ġthat Ġdo Ġnot Ġbelong Ġto Ġthat Ġclass ; </s> False ĠNeg atives Ġare Ġthe Ġarticles Ġclassified Ġas Ġnot Ġbelonging Ġto Ġa Ġcertain Ġclass , Ġthat Ġinstead Ġbelong Ġto Ġthat Ġclass . </s> At Ġa Ġfirst Ġglance , Ġwe Ġobserve Ġthat , Ġacross Ġall Ġthe Ġmodels , Ġthe Ġarticles Ġwith Ġthe Ġlowest Ġclassification Ġvalues , Ġfor Ġboth ĠR OC Ġand ĠF - Measure , Ġare Ġthose Ġlabeled ĠC Ġand ĠGA . ĠAdding Ġthe ĠDomain ĠIn format iveness Ġfeature Ġproduces Ġa Ġclassification , Ġwhich Ġis Ġslightly Ġworse Ġfor ĠC Ġand ĠFA Ġarticles , Ġbut Ġbetter Ġfor Ġthe Ġother Ġfour Ġclasses . ĠThis Ġis Ġparticularly Ġevident Ġfor Ġthe ĠF - Measure Ġof Ġthe Ġarticles Ġof Ġthe ĠGA Ġclass . ĠA Ġnoticeable Ġmajor Ġimprovement Ġis Ġobtained Ġwith Ġthe Ġintroduction Ġof Ġthe Ġfeatures ĠInfo Box Norm Size Ġand ĠCategory Ġin Ġthe ĠMedical ĠDomain Ġmodel . ĠThe ĠR OC ĠArea Ġincreases Ġfor Ġthe Ġarticles Ġof Ġall Ġthe Ġclasses Ġwithin Ġthe ĠFull ĠMedical ĠDomain , Ġwhile Ġthe ĠF - Measure Ġis Ġalways Ġbetter Ġthan Ġthe ĠBas eline Ġand Ġalmost Ġalways Ġbetter Ġthan Ġthe ĠMedical ĠDomain . </s> The Ġsize Ġof Ġan Ġarticle , Ġexpressed Ġeither Ġas Ġthe Ġword Ġcount , Ġanalyzed Ġin ĠB IB REF 20 Ġ, Ġor Ġas Ġthe Ġarticle Ġlength , Ġas Ġdone Ġhere , Ġappears Ġa Ġvery Ġstrong Ġfeature , Ġable Ġto Ġdiscriminate Ġthe Ġarticles Ġbelonging Ġto Ġthe Ġhighest Ġand Ġlowest Ġquality Ġclasses . ĠThis Ġis Ġtestified Ġalso Ġby Ġthe Ġresults Ġachieved Ġexploiting Ġthe Ġbaseline Ġmodel Ġof ĠB IB REF 7 Ġ, Ġwhich Ġpoorly Ġsucceeds Ġin Ġdiscriminating Ġthe Ġarticles Ġof Ġthe Ġintermediate Ġquality Ġclasses , Ġwhile Ġachieving Ġgood Ġresults Ġfor ĠStub Ġand ĠFA . ĠHere , Ġthe Ġnewly Ġintroduced Ġfeatures Ġhave Ġa Ġpredominant Ġeffect Ġon Ġthe Ġarticles Ġof Ġthe Ġhighest Ġclasses . ĠThis Ġcould Ġbe Ġjustified Ġby Ġthe Ġfact Ġthat Ġthose Ġarticles Ġcontain , Ġon Ġaverage , Ġmore Ġtext Ġand , Ġthen , ĠN LP - based Ġfeatures Ġcan Ġexploit Ġmore Ġwords Ġbelonging Ġto Ġa Ġspecific Ġdomain . </s> Then , Ġwe Ġobserve Ġthat Ġthe ĠR OC ĠArea Ġand Ġthe ĠF - Measure Ġare Ġnot Ġtightly Ġcoupled Ġ( name ly : Ġhigh Ġvalues Ġfor Ġthe Ġfirst Ġmetric Ġcan Ġcorrespond Ġto Ġlow Ġvalues Ġfor Ġthe Ġsecond Ġone , Ġsee Ġfor Ġexample ĠC Ġand ĠGA ): Ġthis Ġis Ġdue Ġto Ġthe Ġnature Ġof Ġthe ĠR OC ĠArea , Ġthat Ġis Ġaffected Ġby Ġthe Ġdifferent Ġsizes Ġof Ġthe Ġconsidered Ġclasses . ĠAs Ġan Ġexample , Ġwe Ġcan Ġobserve Ġthat Ġthe Ġbaseline Ġmodel Ġhas Ġthe Ġsame ĠR OC ĠArea Ġvalue Ġfor Ġthe Ġarticles Ġof Ġboth Ġclass ĠB Ġand Ġclass ĠGA , Ġwhile Ġthe ĠF - Measure Ġof Ġarticles Ġof Ġclass ĠB Ġis Ġ0 . 282 Ġhigher Ġthan Ġthat Ġof Ġclass ĠGA . </s> Finally , Ġthe Ġresults Ġconfirm Ġthat Ġthe Ġadoption Ġof Ġdomain - based Ġfeatures Ġand , Ġin Ġgeneral , Ġof Ġfeatures Ġthat Ġleverage ĠN LP , Ġhelp Ġto Ġdistinguish Ġbetween Ġarticles Ġin Ġthe Ġlowest Ġclasses Ġand Ġarticles Ġin Ġthe Ġhighest Ġclasses , Ġas Ġhighlighted Ġin Ġbold Ġin ĠTable Ġ5 Ġ. ĠWe Ġnotice Ġalso Ġthat Ġexploiting Ġthe Ġfull Ġmedical Ġdomain Ġleads Ġus Ġto Ġthe Ġachievement Ġof Ġthe Ġbest Ġresults . </s> Even Ġif Ġpreliminary , Ġwe Ġbelieve Ġthat Ġthe Ġresults Ġare Ġpromising Ġand Ġcall Ġboth Ġfor Ġfeatures ' Ġfurther Ġrefinement Ġand Ġnovel Ġfeatures , Ġable Ġto Ġdiscriminate Ġamong Ġthe Ġintermediate Ġclasses Ġtoo . </s> Related Ġwork </s> Aut omatic Ġquality Ġevaluation Ġof ĠWikipedia Ġarticles Ġhas Ġbeen Ġaddressed Ġin Ġprevious Ġworks Ġwith Ġboth Ġun super vised Ġand Ġsupervised Ġlearning Ġapproaches . ĠThe Ġcommon Ġidea Ġof Ġmost Ġof Ġthe Ġexisting Ġwork Ġis Ġto Ġidentify Ġa Ġfeature Ġset , Ġhaving Ġas Ġa Ġstarting Ġpoint Ġthe ĠWikipedia Ġproject Ġguidelines , Ġto Ġbe Ġexploited Ġwith Ġthe Ġobjective Ġin Ġmind Ġto Ġdistinguish ĠFeatured ĠArticles . ĠIn ĠB IB REF 21 Ġ, ĠSt vil ia Ġet Ġal . Ġidentify Ġa Ġrelevant Ġset Ġof Ġfeatures , Ġincluding Ġling ual , Ġstructural , Ġhistorical Ġand Ġrep ut ational Ġaspects Ġof Ġeach Ġarticle . ĠThey Ġshow Ġthe Ġeffectiveness Ġof Ġtheir Ġmetrics Ġby Ġapplying Ġboth Ġclust ering Ġand Ġclassification . ĠAs Ġa Ġresult , Ġmore Ġthan Ġ90 % Ġof ĠFA Ġare Ġcorrectly Ġidentified . </s> Bl umen stock ĠB IB REF 20 Ġinspect s Ġthe Ġrelevance Ġof Ġthe Ġword - count Ġfeature Ġat Ġeach Ġquality Ġstage , Ġshowing Ġthat Ġit Ġcan Ġplay Ġa Ġvery Ġimportant Ġrole Ġin Ġthe Ġquality Ġassessment Ġof ĠWikipedia Ġarticles . ĠOnly Ġusing Ġthis Ġfeature , Ġthe Ġauthor Ġachieves Ġa ĠF - me asure Ġof Ġ0 . 9 02 Ġin Ġthe Ġtask Ġof Ġclass ifying Ġfeatured Ġarticles Ġand Ġ0 . 98 3 Ġin Ġthe Ġtask Ġof Ġclass ifying Ġnon Ġfeatured Ġarticles . ĠThe Ġbest Ġresults Ġof Ġthe Ġinvestigation Ġare Ġachieved Ġwith Ġa Ġclass ifier Ġbased Ġon Ġa Ġneural Ġnetwork Ġimplemented Ġwith Ġa Ġmulti - layer Ġpercept ron . </s> In ĠB IB REF 22 Ġ, Ġthe Ġauthors Ġtry Ġto Ġanalyze Ġthe Ġfactors Ġaffecting Ġthe Ġquality Ġof ĠWikipedia Ġarticles , Ġwith Ġrespect Ġto Ġtheir Ġquality Ġclass . ĠThe Ġauthors Ġevaluate Ġa Ġset Ġof Ġ28 Ġfeatures , Ġover Ġa Ġrandom Ġsample Ġof Ġ500 ĠWikipedia Ġarticles , Ġby Ġweighing Ġeach Ġmetric Ġin Ġdifferent Ġstages Ġusing Ġneural Ġnetworks . ĠFind ings Ġare Ġthat Ġlinguistic Ġfeatures Ġweigh Ġmore Ġin Ġthe Ġlowest Ġquality Ġclasses , Ġand Ġstructural Ġfeatures , Ġalong Ġwith Ġhistorical Ġones , Ġbecome Ġmore Ġimportant Ġas Ġthe Ġarticle Ġquality Ġimproves . ĠTheir Ġresults Ġindicate Ġthat Ġthe Ġinformation Ġquality Ġis Ġmainly Ġaffected Ġby Ġcomple teness , Ġand Ġto Ġbe ĠâĢ ľ well - written " Ġis Ġa Ġbasic Ġrequirement Ġin Ġthe Ġinitial Ġstage . ĠInstead , Ġreputation Ġof Ġauthors Ġor Ġeditors Ġis Ġnot Ġso Ġimportant Ġin ĠWikipedia Ġbecause Ġof Ġits Ġhorizontal Ġstructure . ĠIn ĠB IB REF 23 Ġ, Ġthe Ġauthors Ġconsider Ġthe Ġquality Ġof Ġthe Ġdata Ġin Ġthe Ġinf ob ox es Ġof ĠWikipedia , Ġfinding Ġa Ġcorrelation Ġbetween Ġthe Ġquality Ġof Ġinformation Ġin Ġthe Ġinf ob ox Ġand Ġthe Ġarticle Ġitself . </s> In ĠB IB REF 7 Ġ, Ġthe Ġauthors Ġdeal Ġwith Ġthe Ġproblem Ġof Ġdiscriminating Ġbetween Ġtwo Ġlarge Ġclasses , Ġnamely ĠNeed Work , ĠGood Enough Ġ( including Ġin ĠGood Enough Ġboth ĠGA Ġand ĠFA ), Ġin Ġorder Ġto Ġidentify Ġwhich Ġarticles Ġneed Ġfurther Ġrevisions Ġfor Ġbeing Ġfeatured . ĠThey Ġalso Ġintroduce Ġnew Ġcomposite Ġfeatures , Ġthose Ġthat Ġwe Ġhave Ġreferred Ġto Ġas Ġan ĠâĢ ľ action able Ġmodel âĢ Ŀ Ġin ĠSection Ġ" Bas eline : Ġthe Ġaction able Ġmodel " Ġ. ĠThey Ġobtain Ġgood Ġclassification Ġresults , Ġwith Ġa ĠF - me asure Ġof Ġ0 . 8 76 Ġin Ġtheir Ġbest Ġconfiguration . ĠThey Ġalso Ġtry Ġclassification Ġfor Ġall Ġthe Ġseven Ġquality Ġclasses , Ġas Ġdone Ġin Ġthis Ġwork , Ġusing Ġa Ġrandom Ġforest Ġclass ifier Ġwith Ġ100 Ġtrees , Ġwith Ġa Ġreduced Ġset Ġof Ġfeatures . ĠThe Ġpoor Ġresults Ġ( an Ġaverage ĠF - me asure Ġof Ġ0 . 425 ) Ġhighlights Ġthe Ġhardness Ġof Ġthis Ġfine - gr ained Ġclassification . ĠIn Ġthis Ġpaper , Ġwe Ġaddress Ġthis Ġlast Ġtask Ġin Ġa Ġnovel Ġway , Ġby Ġintroducing Ġdomain Ġfeatures , Ġspecially Ġdealing Ġwith Ġthe Ġmedical Ġdomain . ĠThe Ġresults Ġof Ġthe Ġinvestigation Ġare Ġpromising . </s> Recent Ġstudies Ġspecifically Ġaddress Ġthe Ġquality Ġof Ġmedical Ġinformation Ġ( in ĠWikipedia Ġas Ġwell Ġas Ġin Ġother Ġresources ): Ġin ĠB IB REF 24 Ġand ĠB IB REF 25 Ġ, Ġthe Ġauthors Ġdebate Ġif ĠWikipedia Ġis Ġa Ġreliable Ġlearning Ġresource Ġfor Ġmedical Ġstudents , Ġevaluating Ġarticles Ġon Ġrespiratory Ġtopics Ġand Ġcardiovascular Ġdiseases . ĠThe Ġevaluation Ġis Ġcarried Ġout Ġby Ġexploiting ĠDIS C ERN , Ġa Ġtool Ġevaluating Ġread ability Ġof Ġarticles . ĠIn ĠB IB REF 26 Ġthe Ġauthors Ġprovide Ġnovel Ġsolutions Ġfor Ġmeasure Ġthe Ġquality Ġof Ġmedical Ġinformation Ġin ĠWikipedia , Ġby Ġadopting Ġan Ġun super vised Ġapproach Ġbased Ġon Ġthe ĠAnaly tic ĠHier achy ĠProcess , Ġa Ġmulti - crit eria Ġdecision Ġmaking Ġtechnique ĠB IB REF 27 Ġ. ĠThe Ġwork Ġin ĠB IB REF 28 Ġaims Ġto Ġprovide Ġthe Ġweb Ġsurf ers Ġa Ġnumerical Ġindication Ġof ĠQuality Ġof ĠMedical ĠWeb ĠSites . ĠIn Ġparticular Ġin ĠB IB REF 28 Ġthe Ġauthor Ġproposes Ġan Ġindex Ġto Ġmake ĠIQ Ġjudgment Ġof Ġthe Ġcontent Ġand Ġof Ġits Ġreliability , Ġto Ġgive Ġthe Ġso Ġcalled ĠâĢ ľ surface Ġmarkers âĢ Ŀ Ġand ĠâĢ ľ trust Ġindicator âĢ Ŀ . ĠA Ġsimilar Ġmeasurement Ġis Ġconsidered Ġin ĠB IB REF 29 Ġ. Ġwhere Ġthe Ġauthors Ġpresent Ġan Ġempirical Ġanalysis Ġthat Ġsuggests Ġthe Ġneed Ġto Ġdefine Ġgenre - specific Ġtemplates Ġfor Ġquality Ġevaluation Ġand Ġto Ġdevelop Ġmodels Ġfor Ġan Ġautomatic Ġgenre - based Ġclassification Ġof Ġhealth Ġinformation ĠWeb Ġpages . ĠIn Ġaddition , Ġthe Ġstudy Ġshows Ġthat Ġconsumers Ġmay Ġlack Ġthe Ġmotivation Ġor Ġliteracy Ġskills Ġto Ġevaluate Ġthe Ġinformation Ġquality Ġof Ġhealth ĠWeb Ġpages . ĠClearly , Ġthis Ġfurther Ġhighlights Ġthe Ġcrucial ity Ġto Ġdevelop Ġaccessible Ġautomatic Ġinformation Ġquality Ġevaluation Ġtools Ġand Ġont ologies . ĠOur Ġwork Ġmoves Ġtowards Ġthe Ġgoal , Ġby Ġspecifically Ġconsidering Ġdomain - relevant Ġfeatures Ġand Ġfeaturing Ġan Ġautomatic Ġclassification Ġtask Ġspanning Ġover Ġmore Ġthan Ġtwo Ġclasses . </s> Con clusions </s> In Ġthis Ġwork , Ġwe Ġaimed Ġto Ġprovide Ġa Ġfine Ġgr ained Ġclassification Ġmechanism Ġfor Ġall Ġthe Ġquality Ġclasses Ġof Ġthe Ġarticles Ġof Ġthe ĠWikipedia ĠMedical ĠPortal . ĠThe Ġidea Ġwas Ġto Ġpropose Ġan Ġautomatic Ġinstrument Ġfor Ġhelping Ġthe Ġreviewers Ġto Ġunderstand Ġwhich Ġarticles Ġare Ġthe Ġless Ġwork - dem anding Ġpapers Ġto Ġpass Ġto Ġnext Ġquality Ġstage . ĠWe Ġfocused Ġon Ġan Ġaction able Ġmodel , Ġnamely Ġwhose Ġfeatures Ġare Ġrelated Ġto Ġthe Ġcontent Ġof Ġthe Ġarticles , Ġso Ġthat Ġthey Ġcan Ġalso Ġdirectly Ġsuggest Ġstrategies Ġfor Ġimproving Ġa Ġgiven Ġarticle . ĠAn Ġimportant Ġand Ġnovel Ġaspect Ġof Ġour Ġclass ifier , Ġwith Ġrespect Ġto Ġprevious Ġworks , Ġis Ġthe Ġleveraging Ġof Ġfeatures Ġextracted Ġfrom Ġthe Ġspecific , Ġmedical Ġdomain , Ġwith Ġthe Ġhelp Ġof ĠNatural ĠLanguage ĠProcessing Ġtechniques . ĠAs Ġthe Ġresults Ġof Ġour Ġexperiments Ġconfirm , Ġconsidering Ġspecific Ġdomain - based Ġfeatures , Ġlike ĠDomain ĠIn format iveness Ġand ĠCategory , Ġcan Ġeventually Ġhelp Ġand Ġimprove Ġthe Ġautomatic Ġclassification Ġresults . ĠSince Ġthe Ġresults Ġare Ġencouraging , Ġas Ġfuture Ġwork Ġwe Ġwill Ġevaluate Ġother Ġfeatures Ġbased Ġon Ġthe Ġspecific Ġmedical Ġdomain . ĠMoreover , Ġwe Ġare Ġplanning Ġto Ġextend Ġour Ġidea , Ġto Ġinclude Ġand Ġcompare Ġalso Ġother Ġnon Ġmedical Ġarticles Ġ( thus , Ġextending Ġthe Ġwork Ġto Ġinclude Ġother Ġdomains ), Ġin Ġorder Ġto Ġfurther Ġvalidate Ġour Ġapproach . </s> Acknowled gments . ĠThe Ġresearch Ġleading Ġto Ġthese Ġresults Ġhas Ġbeen Ġpartially Ġfunded Ġby Ġthe ĠRegist ro . it Ġproject ĠMy ĠInformation ĠBubble ĠM IB .
Introduction As observed by a recent article of Nature News BIBREF0 , “Wikipedia is among the most frequently visited websites in the world and one of the most popular places to tap into the world's scientific and medical information". Despite the huge amount of consultations, open issues still threaten a fully confident fruition of the popular online open encyclopedia. A first issue relates to the reliability of the information available: since Wikipedia can be edited by anyone, regardless of their level of expertise, this tends to erode the average reputation of the sources, and, consequently, the trustworthiness of the contents posted by those sources. In an attempt to fix this shortcoming, Wikipedia has recently enlisted the help of scientists to actively support the editing on Wikipedia BIBREF0 . Furthermore, lack of control may lead to the publication of fake Wikipedia pages, which distort the information by inserting, e.g., promotional articles and promotional external links. Fighting vandalism is one of the main goals of the Wikimedia Foundation, the nonprofit organization that supports Wikipedia: machine learning techniques have been considered to offer a service to “judge whether an edit was made in good faith or not" BIBREF1 . Nonetheless, in the past recent time, malicious organisations have acted disruptively with purposes of extortion - see, e.g., the recent news on the uncovering of a blackmail network of accounts, which threatened celebrities with the menace of inserting offending information on their Wikipedia pages. Secondly, articles may suffer from readability issues: achieving a syntactical accuracy that helps the reader with a fluid reading experience is —quite obviously— a property which articles should fulfill. Traditionally, the literature has widely adopted well known criteria, as the “Flesch-Kincaid" measure" BIBREF2 , to automatically assess readability in textual documents. More recently, new techniques have been proposed too, for assessing the readability of natural languages (see, e.g., BIBREF3 for the Italian use case, BIBREF4 for the Swedish one, BIBREF5 for English). In this paper, we face the quest for quality assessment of a Wikipedia article, in an automatic way that comprehends not only readability and reliability criteria, but also additional parameters testifying completeness of information and coherence with the content one expects from an article dealing with specific topics, plus sufficient insights for the reader to elaborate further on some argument. The notion of data quality we deal with in the paper is coherent with the one suggested by recent contributions (see, e.g., BIBREF6 ), which points out like the quality of Web information is strictly connected to the scope for which one needs such information. Our intuition is that groups of articles related to a specific topic and falling within specific scopes are intrinsically different from other groups on different topics within different scopes. We approach the article evaluation through machine learning techniques. Such techniques are not new to be employed for automatic evaluation of articles quality. As an example, the work in BIBREF7 exploits classification techniques based on structural and linguistic features of an article. Here, we enrich that model with novel features that are domain-specific. As a running scenario, we focus on the Wikipedia medical portal. Indeed, facing the problems of information quality and ensuring high and correct levels of informativeness is even more demanding when health aspects are involved. Recent statistics report that Internet users are increasingly searching the Web for health information, by consulting search engines, social networks, and specialised health portals, like that of Wikipedia. As pointed out by the 2014 Eurobarometer survey on European citizens' digital health literacy, around six out of ten respondents have used the Internet to search for health-related information. This means that, although the trend in digital health literacy is growing, there is also a demand for a qualified source where people can ask and find medical information which, to an extent, can provide the same level of familiarity and guarantees as those given by a doctor or a health professional. We anticipate here that leveraging new domain-specific features is in line with this demand of articles quality. Moreover, as the outcomes of our experiments show, they effectively improve the classification results in the hard task of multi-class assessment, especially for those classes that other automatic approaches worst classify. Remarkably, our proposal is general enough to be easily extended to other domains, in addition to the medical one. Section "Dataset" first describes the structure of the articles present in the medical portal. Then, it gives details on the real data used in the experiments, which are indeed articles extracted from the medical portal and labeled according to the manual assessment by the Wikimedia project. Section "Baseline: the actionable model" briefly presents the actionable model in BIBREF7 : we adopt it as the baseline for our analysis. In Section "The medical domain model" , we present the domain-specific, medical model we newly adopt in this paper as an extension of the baseline. The extended model includes features specifically extracted from the medical domain. One novel feature is based on the article textual content. Section "Bio-medical entities" presents the process which its extraction relies on, with a non trivial analysis of natural language and domain knowledge. Section "Experiments and results" presents experiments and results, with a comparison of the baseline model with the new one. In Section "Related work" , we survey related work in the area and in Section "Conclusions" we conclude the paper. Dataset We consider the dataset consisting of the entire collection of articles of the Wikipedia Medicine Portal, updated at the end of 2014. Wikipedia articles are written according to the Media Wiki markup language, a HTML-like language. Among the structural elements of one page, which differs from standard HTML pages, there are i) the internal links, i.e., links to other Wikipedia pages, different from links to external resources); ii) categories, which represent the Media Wiki categories a page belongs to: they are encoded in the part of text within the Media Wiki “categories" tag in the page source, and iii) informative boxes, so called “infoboxes", which summarize in a structured manner some peculiar pieces of information related the topic of the article. The category values for the articles in the medical portal span over the ones listed at https://en.wikipedia.org/wiki/Portal:Medicine. Examples of categories, which appear at the bottom of each Wikipedia page, are in Fig. 1 . Infoboxes of the medical portal feature medical content and standard coding. As an example, Fig. 2 shows the infobox in the Alzheimer's disease page of the portal. The infobox contains explanatory figures and text denoting peculiar characteristics of the disease and the value for the standard code of such disease (ICD9, as for the international classification of the disease). Thanks to WikiProject Medicine, the dataset of articles we collected from the Wikipedia Medicine Portal has been manually labeled into seven quality classes. They are ordered as Stub, Start, C, B, A, Good Article (GA), Featured Article (FA). The Featured and Good article classes are the highest ones: to have those labels, an article requires a community consensus and an official review by selected editors, while the other labels can be achieved with reviews from a larger, even controlled, set of editors. Actually, none of the articles in the dataset is labeled as A, thus, in the following, we do not consider that class, restricting the investigation to six classes. At the date of our study, we were able to gather 24,362 rated documents. Remarkably, only a small percentage of them (1%) is labeled as GA and FA. Indeed, the distribution of the articles among the classes is highly skewed. There are very few (201) articles for the highest quality classes (FA and GA), while the vast majority (19,108) belongs to the lowest quality ones (Stub and Start). This holds not only for the medical portal. Indeed, it is common in all Wikipedia, where, on average, only one article in every thousand is a Featured one. In Section "Experiments and results" , we will adopt a set of machine-learning classifiers to automatically label the articles into the quality classes. Dealing with imbalanced classes is a common situation in many real applications of classification learning: healthy patients over the population, fraudulent actions over daily genuine transactions, and so on. Without any countermeasure, common classifiers tend to correctly identify only articles belonging to the majority classes, clearly leading to severe mis-classification of the minority classes, since typical learning algorithms strive to maximize the overall prediction accuracy. To reduce the disequilibrium among the size of the classes, we have first randomly sampled the articles belonging to the most populated classes. Then, we have performed some further elaboration, as shown in the following. Many studies have been conducted to improve learning algorithms accuracy in presence of imbalanced data BIBREF8 . For the current work, we have considered one of the most popular approaches, namely the Synthetic Sampling with Data Generation, detailed in BIBREF9 . It consists in generating synthetic instances from the minority classes, to balance the overall dataset. The approach has been broadly applied to problems relying on NLP features, see, e.g., BIBREF10 . In our case, we resampled the input data set by applying the Synthetic Minority Oversampling TEchnique (SMOTE), with percentage 40% for GA and 180%, for FA. In particular, the steps to oversample are the following: Table 1 shows the number of articles in the dataset, divided per class, as well as the random samples we have considered for our study. The experiments presented in Section "Experiments and results" are based on the articles of the right-hand column in the table. Baseline: the actionable model We apply a multi-class classification approach to label the articles of the sampled dataset into the six WikiProject quality classes. In order to have a baseline, we first apply the state of the art model proposed in BIBREF7 to the dataset. The “actionable model" in BIBREF7 focuses on five linguistic and structural features and it weighs them as follows: where In order to evaluate such model over our dataset, we have extracted from our the dataset the above mentioned features. We have measured ArticleLength, NumWikilinks and NumBrokenWikilinks as suggested in BIBREF11 . As shown in Figure 3 , the actionable model features have been extracted applying simple scripts mainly based on regular expressions (the “regexp extractor" block), which process the whole HTML code of an article (free text plus media wiki tags). The regexp extractor relies on Python BeautifulSoup for extracting HTML structures and excerpts of the textual content within the MediaWiki tags and on nltk libraries for basic NLP analysis. In details, nltk has been used for computing the InfoNoise feature, whose computation includes the stopwords removal, following the Porter Stopwords Corpus available through nltk BIBREF12 . The classification results according to the baseline model are reported in Section "Experiments and results" . The medical domain model Here, we improve the baseline model with novel and specifically crafted features that rely on the medical domain and that capture details on the specific content of an article. As shown in Figure 3 , medical model features, the bio-medical entities, have been extracted from the free text only, exploiting advanced NLP techniques and using domain dictionaries. In details, we newly define and extract from the dataset the following novel features: The idea of considering infoboxes is not novel: for example, in BIBREF7 the authors noticed that the presence of an infobox is a characteristic featured by good articles. However, in the specific case of the Medicine Portal, the presence of an infobox does not seem strictly related to the quality class the article belongs to (according to the manual labelling). Indeed, it is recurrent that articles, spanning all classes, have an infobox, containing a schematic synthesis of the article. In particular, pages with descriptions of diseases usually have an infobox with the medical standard code of the disease (i.e., IDC-9 and IDC-10), as in Figure 2 . As done for the baseline, also the first two features of the medical model have been extracted with ad hoc Python scripts, extracting HTML structures and excerpts of the textual content within the MediaWiki tags. For their extraction of the bio-medical entities, we consider the textual part of the article only, obtained after removing the MediaWiki tags, and we apply a NLP analysis, which is presented in Section "Bio-medical entities" . Infobox-based feature We have calculated the Infobox size as the base 10 log of the bytes of data contained within the mediawiki tags that wrap an infobox, and we have normalized it with respect to the ArticleLength, introduced in Section "Baseline: the actionable model" . Category-based feature We have leveraged the categories assigned to articles in Wikipedia, in particular relating to the medicine topics available at https://en.wikipedia.org/wiki/Portal:Medicine. We have defined 4 upper level categories of our interest: A anatomy: an article is about anatomy; B biography: an article is a biography of someone or tell the history of something; D disorder: it is about a disorder; F first aid: it reports information for first aid or emergency contacts; O other: none of the above. We have matched the article's text within the MediaWiki categories tag with an approximate list of keywords that are related to our category of interest, as reported in Table 2 . Bio-medical entities In the literature, there are several methods available for extracting bio-medical entities from a text (i.e., from medical notes and/or articles). We refer to BIBREF13 for an overview of valuable existing techniques. In this work, we have adopted a dictionary-based approach, which exploits lexical features and domain knowledge extracted from the Unified Medical Languages System (UMLS) Metathesaurus BIBREF14 . The approach has been proposed for the Italian language in a past work BIBREF15 . Since the approach combines the usage of linguistic analysis and domain resources, we were able to conveniently adapt it for the English language, being both the linguistic pipeline and UMLS available for multiple languages (including English and Italian). Dictionary-based approaches have been proved valid for the task of entities' extraction, see, for example, another well known, similar approach to the one adopted here, i.e., Metamap. It is worth noting like, even though dictionary-based approaches could be less precise than Named Entity Recognition BIBREF13 , in our context even an approximate solution is enough, since we are not annotating medical records. Instead, we are quantifying the mole of inherent information within a text. Reference dictionary Several ontologies or taxonomies related to the medical domain are available in English. To build a medical dictionary, we have extracted definitions of medical entities from the Unified Medical Languages System (UMLS) Metathesaurus BIBREF14 . UMLS integrates bio-medical resources, such as SNOMED-CT that provides the core terminology for electronic health records. In addition, UMLS also provides a semantic network where each entity in the Metathesaurus has an assigned Concept Unique Identifier (CUI) and it is semantically typed. From UMLS, we have extracted the entries belonging to the following SNOMED-CT semantic groups: Treatment, Sign or Symptom, Disease or Syndrome, Body Parts, Organs, or Organ Components, Pathologic Function, and Mental or Behavioral Dysfunction, for a total of more than one million entries, as shown in Table 3 (where the two last semantic groups have been grouped together, under Disorder). Furthermore, we have extracted common Drugs and Active Ingredients definitions from RxNorm, accessed by RxTerm. Starting from the entries in Table 3 , we have also computed approximate definitions, exploiting syntactic information of the same entries. In details, we have pre-processed the entries by mean of the Tanl pipeline BIBREF16 , a suite of modules for text analytics and NLP, based on machine learning. Preprocessing has consisted in first dividing the entries into single word forms. Then, for each form, we have identified the lemma (when available) and the part of speech (POS). Thus, we have created an approximate definition that consists in using only the lemma and cleaning the text, excluding punctuation, prepositions and articles. Also, approximate definitions have been normalized by lowercasing each word. As an example, the Disorder entry “aneurysm of the vein of galen" has been stored in the dictionary, along with its approximate definition “aneurysm vein galen". Extraction of bio-medical entities We have extracted the bio-medical entities present in the Wikipedia medical articles through a n-gram-based technique. A pre-processing phase occurs in a similar way as for the dictionary composition. Given a Wikipedia article written in English, we have pre-processed the textual part through the Tanl pipeline. Similar to what described in Section "Reference dictionary" for the reference dictionary, we have first divided the text in sentences and the sentences into single word forms. For each form, we have considered the lemma (when available) and the part of speech (POS). For instance, starting from an example sentence extracted from the Wikipedia page on the Alzheimer's disease: “Other risk factors include a history of head injuries, depression, or hypertension.", we have obtained the annotation shown in Figure 4 . As in the case of the dictionary, each word in the text has been lowercasing. After pre-processing the text of each article, we have attempted to match each n-gram (with n between 1 and 10) in the corpus with the entries in the extended dictionary. We both attempt an exact match and an approximate match, the latter removing prepositions, punctuations and articles from the n-grams. Approximate matching leads to several advantages. Indeed, exploiting the text pre-processing allows to identify dictionary definitions present in the text, even when the number differs. As an example, the dictionary definition “injury" will match with “injuries", mentioned in the text, because in the approximation one can consider the lemmas. Further, considering the POS allows to identify mentions when interleaved by prepositions, articles, and conjunctions that change the form but do not alter the meaning. As an example, the approximate definition “aneurysm vein galen” will match also with the following n-gram: “the aneurysm and vein of galen", if present in the text. Experiments and results In this section, we describe the experiments and report the results for the classification of Wikipedia medical articles into the six classes of the Wikipedia Medicine Portal. We compare the results obtained adopting four different classifiers: the actionable model in BIBREF7 and three classifiers that leverage the ad-hoc features from the medical domain discussed in the previous sections. All the experiments were realized within the Weka framework BIBREF17 and validated through 10 fold cross-validation. For each experiment, we relied on the dataset presented in Section "Dataset" , and specifically, on that obtained after sampling the majority classes and oversampling the minority ones (right-hand column in Table 1 ). The dataset serves both as training and test set for the classifiers. Moreover, to take into account the imbalanced data, we have applied several classification algorithms and, for the sake of conciseness, hereafter we report only the best results we have achieved. In particular, we have experimented with bagging, adaptive boosting and random forest and we report the results for the latter only. Classifiers' features In Table 4 , we report a summary of the features for each of the considered models: the baseline model in BIBREF7 and two new models that employ the medical domain features. In the Medical Domain model, we add to the baseline features the Domain Informativeness, as described in Section "The medical domain model" and "Bio-medical entities" . In addition, the Full Medical Domain model also considers the features InfoBoxNormSize and Category. For each of the features, the table also reports the Information Gain, evaluated on the whole dataset (24,362 articles). Information Gain is a well-known metric to evaluate the dependency of one class from a single feature, see, e.g., BIBREF18 . We can observe how the Domain Informativeness feature has a considerably higher infogain value when compared with Informativeness. We anticipate here that this will lead to a more accurate classification results for the highest classes, as reported in the next section. Leading to a greater accuracy is also true for the other two new features that, despite showing lower values of infogain, are able to further improve the classification results, mainly for the articles belonging to the lowest quality classes (Stub and Start). Classification results Table 5 shows the results of our multi-class classification. For each of the classes, we have computed the ROC Area and F-Measure metrics BIBREF19 . The latter, in particular, is usually considered a significant metric in terms of classification, since it combines in one single value all the four indicators that are generally implied for evaluating the classifier performance (i.e., number of True Positives, False Positives, True Negatives and False Negatives). In our scenario, the meaning of the indicators, for each class, are as follows: True Positives are the articles classified as belonging to a certain class, that indeed belong to that class (according to the quality ratings given by the WikiProject Medicine); True Negatives are the articles classified as not belonging to a certain class, that indeed do not belong to that class; False Positives are the articles classified as belonging to a certain class, that do not belong to that class; False Negatives are the articles classified as not belonging to a certain class, that instead belong to that class. At a first glance, we observe that, across all the models, the articles with the lowest classification values, for both ROC and F-Measure, are those labeled C and GA. Adding the Domain Informativeness feature produces a classification, which is slightly worse for C and FA articles, but better for the other four classes. This is particularly evident for the F-Measure of the articles of the GA class. A noticeable major improvement is obtained with the introduction of the features InfoBoxNormSize and Category in the Medical Domain model. The ROC Area increases for the articles of all the classes within the Full Medical Domain, while the F-Measure is always better than the Baseline and almost always better than the Medical Domain. The size of an article, expressed either as the word count, analyzed in BIBREF20 , or as the article length, as done here, appears a very strong feature, able to discriminate the articles belonging to the highest and lowest quality classes. This is testified also by the results achieved exploiting the baseline model of BIBREF7 , which poorly succeeds in discriminating the articles of the intermediate quality classes, while achieving good results for Stub and FA. Here, the newly introduced features have a predominant effect on the articles of the highest classes. This could be justified by the fact that those articles contain, on average, more text and, then, NLP-based features can exploit more words belonging to a specific domain. Then, we observe that the ROC Area and the F-Measure are not tightly coupled (namely: high values for the first metric can correspond to low values for the second one, see for example C and GA): this is due to the nature of the ROC Area, that is affected by the different sizes of the considered classes. As an example, we can observe that the baseline model has the same ROC Area value for the articles of both class B and class GA, while the F-Measure of articles of class B is 0.282 higher than that of class GA. Finally, the results confirm that the adoption of domain-based features and, in general, of features that leverage NLP, help to distinguish between articles in the lowest classes and articles in the highest classes, as highlighted in bold in Table 5 . We notice also that exploiting the full medical domain leads us to the achievement of the best results. Even if preliminary, we believe that the results are promising and call both for features' further refinement and novel features, able to discriminate among the intermediate classes too. Related work Automatic quality evaluation of Wikipedia articles has been addressed in previous works with both unsupervised and supervised learning approaches. The common idea of most of the existing work is to identify a feature set, having as a starting point the Wikipedia project guidelines, to be exploited with the objective in mind to distinguish Featured Articles. In BIBREF21 , Stvilia et al. identify a relevant set of features, including lingual, structural, historical and reputational aspects of each article. They show the effectiveness of their metrics by applying both clustering and classification. As a result, more than 90% of FA are correctly identified. Blumenstock BIBREF20 inspects the relevance of the word-count feature at each quality stage, showing that it can play a very important role in the quality assessment of Wikipedia articles. Only using this feature, the author achieves a F-measure of 0.902 in the task of classifying featured articles and 0.983 in the task of classifying non featured articles. The best results of the investigation are achieved with a classifier based on a neural network implemented with a multi-layer perceptron. In BIBREF22 , the authors try to analyze the factors affecting the quality of Wikipedia articles, with respect to their quality class. The authors evaluate a set of 28 features, over a random sample of 500 Wikipedia articles, by weighing each metric in different stages using neural networks. Findings are that linguistic features weigh more in the lowest quality classes, and structural features, along with historical ones, become more important as the article quality improves. Their results indicate that the information quality is mainly affected by completeness, and to be “well-written" is a basic requirement in the initial stage. Instead, reputation of authors or editors is not so important in Wikipedia because of its horizontal structure. In BIBREF23 , the authors consider the quality of the data in the infoboxes of Wikipedia, finding a correlation between the quality of information in the infobox and the article itself. In BIBREF7 , the authors deal with the problem of discriminating between two large classes, namely NeedWork, GoodEnough (including in GoodEnough both GA and FA), in order to identify which articles need further revisions for being featured. They also introduce new composite features, those that we have referred to as an “actionable model” in Section "Baseline: the actionable model" . They obtain good classification results, with a F-measure of 0.876 in their best configuration. They also try classification for all the seven quality classes, as done in this work, using a random forest classifier with 100 trees, with a reduced set of features. The poor results (an average F-measure of 0.425) highlights the hardness of this fine-grained classification. In this paper, we address this last task in a novel way, by introducing domain features, specially dealing with the medical domain. The results of the investigation are promising. Recent studies specifically address the quality of medical information (in Wikipedia as well as in other resources): in BIBREF24 and BIBREF25 , the authors debate if Wikipedia is a reliable learning resource for medical students, evaluating articles on respiratory topics and cardiovascular diseases. The evaluation is carried out by exploiting DISCERN, a tool evaluating readability of articles. In BIBREF26 the authors provide novel solutions for measure the quality of medical information in Wikipedia, by adopting an unsupervised approach based on the Analytic Hierachy Process, a multi-criteria decision making technique BIBREF27 . The work in BIBREF28 aims to provide the web surfers a numerical indication of Quality of Medical Web Sites. In particular in BIBREF28 the author proposes an index to make IQ judgment of the content and of its reliability, to give the so called “surface markers” and “trust indicator”. A similar measurement is considered in BIBREF29 . where the authors present an empirical analysis that suggests the need to define genre-specific templates for quality evaluation and to develop models for an automatic genre-based classification of health information Web pages. In addition, the study shows that consumers may lack the motivation or literacy skills to evaluate the information quality of health Web pages. Clearly, this further highlights the cruciality to develop accessible automatic information quality evaluation tools and ontologies. Our work moves towards the goal, by specifically considering domain-relevant features and featuring an automatic classification task spanning over more than two classes. Conclusions In this work, we aimed to provide a fine grained classification mechanism for all the quality classes of the articles of the Wikipedia Medical Portal. The idea was to propose an automatic instrument for helping the reviewers to understand which articles are the less work-demanding papers to pass to next quality stage. We focused on an actionable model, namely whose features are related to the content of the articles, so that they can also directly suggest strategies for improving a given article. An important and novel aspect of our classifier, with respect to previous works, is the leveraging of features extracted from the specific, medical domain, with the help of Natural Language Processing techniques. As the results of our experiments confirm, considering specific domain-based features, like Domain Informativeness and Category, can eventually help and improve the automatic classification results. Since the results are encouraging, as future work we will evaluate other features based on the specific medical domain. Moreover, we are planning to extend our idea, to include and compare also other non medical articles (thus, extending the work to include other domains), in order to further validate our approach. Acknowledgments. The research leading to these results has been partially funded by the Registro.it project My Information Bubble MIB.
Is it valid to presume a bad medical wikipedia article should not contain much domain-specific jargon?
6a633811019e9323dc8549ad540550d27aa6d972
Is Ġit Ġvalid Ġto Ġpresume Ġa Ġbad Ġmedical Ġwik ipedia Ġarticle Ġshould Ġnot Ġcontain Ġmuch Ġdomain - specific Ġjargon ?
Un answer able
<s> Is Ġit Ġvalid Ġto Ġpresume Ġa Ġbad Ġmedical Ġwik ipedia Ġarticle Ġshould Ġnot Ġcontain Ġmuch Ġdomain - specific Ġjargon ? </s> Introduction </s> As Ġobserved Ġby Ġa Ġrecent Ġarticle Ġof ĠNature ĠNews ĠB IB REF 0 Ġ, ĠâĢ ľ Wikipedia Ġis Ġamong Ġthe Ġmost Ġfrequently Ġvisited Ġwebsites Ġin Ġthe Ġworld Ġand Ġone Ġof Ġthe Ġmost Ġpopular Ġplaces Ġto Ġtap Ġinto Ġthe Ġworld 's Ġscientific Ġand Ġmedical Ġinformation ". ĠDespite Ġthe Ġhuge Ġamount Ġof Ġconsultations , Ġopen Ġissues Ġstill Ġthreaten Ġa Ġfully Ġconfident Ġfruition Ġof Ġthe Ġpopular Ġonline Ġopen Ġencyclopedia . </s> A Ġfirst Ġissue Ġrelates Ġto Ġthe Ġreliability Ġof Ġthe Ġinformation Ġavailable : Ġsince ĠWikipedia Ġcan Ġbe Ġedited Ġby Ġanyone , Ġregardless Ġof Ġtheir Ġlevel Ġof Ġexpertise , Ġthis Ġtends Ġto Ġer ode Ġthe Ġaverage Ġreputation Ġof Ġthe Ġsources , Ġand , Ġconsequently , Ġthe Ġtrust worthiness Ġof Ġthe Ġcontents Ġposted Ġby Ġthose Ġsources . ĠIn Ġan Ġattempt Ġto Ġfix Ġthis Ġshort coming , ĠWikipedia Ġhas Ġrecently Ġenlisted Ġthe Ġhelp Ġof Ġscientists Ġto Ġactively Ġsupport Ġthe Ġediting Ġon ĠWikipedia ĠB IB REF 0 Ġ. ĠFurthermore , Ġlack Ġof Ġcontrol Ġmay Ġlead Ġto Ġthe Ġpublication Ġof Ġfake ĠWikipedia Ġpages , Ġwhich Ġdistort Ġthe Ġinformation Ġby Ġinserting , Ġe . g ., Ġpromotional Ġarticles Ġand Ġpromotional Ġexternal Ġlinks . ĠFighting Ġvandalism Ġis Ġone Ġof Ġthe Ġmain Ġgoals Ġof Ġthe ĠWikimedia ĠFoundation , Ġthe Ġnonprofit Ġorganization Ġthat Ġsupports ĠWikipedia : Ġmachine Ġlearning Ġtechniques Ġhave Ġbeen Ġconsidered Ġto Ġoffer Ġa Ġservice Ġto ĠâĢ ľ jud ge Ġwhether Ġan Ġedit Ġwas Ġmade Ġin Ġgood Ġfaith Ġor Ġnot " ĠB IB REF 1 Ġ. ĠNonetheless , Ġin Ġthe Ġpast Ġrecent Ġtime , Ġmalicious Ġorganisations Ġhave Ġacted Ġdisrupt ively Ġwith Ġpurposes Ġof Ġextortion Ġ- Ġsee , Ġe . g ., Ġthe Ġrecent Ġnews Ġon Ġthe Ġuncover ing Ġof Ġa Ġblackmail Ġnetwork Ġof Ġaccounts , Ġwhich Ġthreatened Ġcelebrities Ġwith Ġthe Ġmenace Ġof Ġinserting Ġoffending Ġinformation Ġon Ġtheir ĠWikipedia Ġpages . </s> Secondly , Ġarticles Ġmay Ġsuffer Ġfrom Ġread ability Ġissues : Ġachieving Ġa Ġsynt act ical Ġaccuracy Ġthat Ġhelps Ġthe Ġreader Ġwith Ġa Ġfluid Ġreading Ġexperience Ġis ĠâĢĶ quite Ġobviously âĢĶ Ġa Ġproperty Ġwhich Ġarticles Ġshould Ġfulfill . ĠTrad itionally , Ġthe Ġliterature Ġhas Ġwidely Ġadopted Ġwell Ġknown Ġcriteria , Ġas Ġthe ĠâĢ ľ F les ch - K inc aid " Ġmeasure " ĠB IB REF 2 Ġ, Ġto Ġautomatically Ġassess Ġread ability Ġin Ġtextual Ġdocuments . ĠMore Ġrecently , Ġnew Ġtechniques Ġhave Ġbeen Ġproposed Ġtoo , Ġfor Ġassessing Ġthe Ġread ability Ġof Ġnatural Ġlanguages Ġ( see , Ġe . g ., ĠB IB REF 3 Ġfor Ġthe ĠItalian Ġuse Ġcase , ĠB IB REF 4 Ġfor Ġthe ĠSwedish Ġone , ĠB IB REF 5 Ġfor ĠEnglish ). </s> In Ġthis Ġpaper , Ġwe Ġface Ġthe Ġquest Ġfor Ġquality Ġassessment Ġof Ġa ĠWikipedia Ġarticle , Ġin Ġan Ġautomatic Ġway Ġthat Ġcomprehend s Ġnot Ġonly Ġread ability Ġand Ġreliability Ġcriteria , Ġbut Ġalso Ġadditional Ġparameters Ġtestifying Ġcomple teness Ġof Ġinformation Ġand Ġco herence Ġwith Ġthe Ġcontent Ġone Ġexpects Ġfrom Ġan Ġarticle Ġdealing Ġwith Ġspecific Ġtopics , Ġplus Ġsufficient Ġinsights Ġfor Ġthe Ġreader Ġto Ġelaborate Ġfurther Ġon Ġsome Ġargument . ĠThe Ġnotion Ġof Ġdata Ġquality Ġwe Ġdeal Ġwith Ġin Ġthe Ġpaper Ġis Ġcoherent Ġwith Ġthe Ġone Ġsuggested Ġby Ġrecent Ġcontributions Ġ( see , Ġe . g ., ĠB IB REF 6 Ġ), Ġwhich Ġpoints Ġout Ġlike Ġthe Ġquality Ġof ĠWeb Ġinformation Ġis Ġstrictly Ġconnected Ġto Ġthe Ġscope Ġfor Ġwhich Ġone Ġneeds Ġsuch Ġinformation . </s> Our Ġintuition Ġis Ġthat Ġgroups Ġof Ġarticles Ġrelated Ġto Ġa Ġspecific Ġtopic Ġand Ġfalling Ġwithin Ġspecific Ġsc opes Ġare Ġintrinsically Ġdifferent Ġfrom Ġother Ġgroups Ġon Ġdifferent Ġtopics Ġwithin Ġdifferent Ġsc opes . ĠWe Ġapproach Ġthe Ġarticle Ġevaluation Ġthrough Ġmachine Ġlearning Ġtechniques . ĠSuch Ġtechniques Ġare Ġnot Ġnew Ġto Ġbe Ġemployed Ġfor Ġautomatic Ġevaluation Ġof Ġarticles Ġquality . ĠAs Ġan Ġexample , Ġthe Ġwork Ġin ĠB IB REF 7 Ġexploits Ġclassification Ġtechniques Ġbased Ġon Ġstructural Ġand Ġlinguistic Ġfeatures Ġof Ġan Ġarticle . ĠHere , Ġwe Ġenrich Ġthat Ġmodel Ġwith Ġnovel Ġfeatures Ġthat Ġare Ġdomain - specific . ĠAs Ġa Ġrunning Ġscenario , Ġwe Ġfocus Ġon Ġthe ĠWikipedia Ġmedical Ġportal . ĠIndeed , Ġfacing Ġthe Ġproblems Ġof Ġinformation Ġquality Ġand Ġensuring Ġhigh Ġand Ġcorrect Ġlevels Ġof Ġinform at iveness Ġis Ġeven Ġmore Ġdemanding Ġwhen Ġhealth Ġaspects Ġare Ġinvolved . ĠRecent Ġstatistics Ġreport Ġthat ĠInternet Ġusers Ġare Ġincreasingly Ġsearching Ġthe ĠWeb Ġfor Ġhealth Ġinformation , Ġby Ġconsulting Ġsearch Ġengines , Ġsocial Ġnetworks , Ġand Ġspecial ised Ġhealth Ġportals , Ġlike Ġthat Ġof ĠWikipedia . ĠAs Ġpointed Ġout Ġby Ġthe Ġ2014 ĠEuro bar ometer Ġsurvey Ġon ĠEuropean Ġcitizens ' Ġdigital Ġhealth Ġliteracy , Ġaround Ġsix Ġout Ġof Ġten Ġrespondents Ġhave Ġused Ġthe ĠInternet Ġto Ġsearch Ġfor Ġhealth - related Ġinformation . ĠThis Ġmeans Ġthat , Ġalthough Ġthe Ġtrend Ġin Ġdigital Ġhealth Ġliteracy Ġis Ġgrowing , Ġthere Ġis Ġalso Ġa Ġdemand Ġfor Ġa Ġqualified Ġsource Ġwhere Ġpeople Ġcan Ġask Ġand Ġfind Ġmedical Ġinformation Ġwhich , Ġto Ġan Ġextent , Ġcan Ġprovide Ġthe Ġsame Ġlevel Ġof Ġfamiliarity Ġand Ġguarantees Ġas Ġthose Ġgiven Ġby Ġa Ġdoctor Ġor Ġa Ġhealth Ġprofessional . </s> We Ġanticipate Ġhere Ġthat Ġleveraging Ġnew Ġdomain - specific Ġfeatures Ġis Ġin Ġline Ġwith Ġthis Ġdemand Ġof Ġarticles Ġquality . ĠMoreover , Ġas Ġthe Ġoutcomes Ġof Ġour Ġexperiments Ġshow , Ġthey Ġeffectively Ġimprove Ġthe Ġclassification Ġresults Ġin Ġthe Ġhard Ġtask Ġof Ġmulti - class Ġassessment , Ġespecially Ġfor Ġthose Ġclasses Ġthat Ġother Ġautomatic Ġapproaches Ġworst Ġclassify . ĠRem ark ably , Ġour Ġproposal Ġis Ġgeneral Ġenough Ġto Ġbe Ġeasily Ġextended Ġto Ġother Ġdomains , Ġin Ġaddition Ġto Ġthe Ġmedical Ġone . </s> Section Ġ" Dat as et " Ġfirst Ġdescribes Ġthe Ġstructure Ġof Ġthe Ġarticles Ġpresent Ġin Ġthe Ġmedical Ġportal . ĠThen , Ġit Ġgives Ġdetails Ġon Ġthe Ġreal Ġdata Ġused Ġin Ġthe Ġexperiments , Ġwhich Ġare Ġindeed Ġarticles Ġextracted Ġfrom Ġthe Ġmedical Ġportal Ġand Ġlabeled Ġaccording Ġto Ġthe Ġmanual Ġassessment Ġby Ġthe ĠWikimedia Ġproject . ĠSection Ġ" Bas eline : Ġthe Ġaction able Ġmodel " Ġbriefly Ġpresents Ġthe Ġaction able Ġmodel Ġin ĠB IB REF 7 Ġ: Ġwe Ġadopt Ġit Ġas Ġthe Ġbaseline Ġfor Ġour Ġanalysis . ĠIn ĠSection Ġ" The Ġmedical Ġdomain Ġmodel " Ġ, Ġwe Ġpresent Ġthe Ġdomain - specific , Ġmedical Ġmodel Ġwe Ġnewly Ġadopt Ġin Ġthis Ġpaper Ġas Ġan Ġextension Ġof Ġthe Ġbaseline . ĠThe Ġextended Ġmodel Ġincludes Ġfeatures Ġspecifically Ġextracted Ġfrom Ġthe Ġmedical Ġdomain . ĠOne Ġnovel Ġfeature Ġis Ġbased Ġon Ġthe Ġarticle Ġtextual Ġcontent . ĠSection Ġ" Bio - medical Ġentities " Ġpresents Ġthe Ġprocess Ġwhich Ġits Ġextraction Ġrelies Ġon , Ġwith Ġa Ġnon Ġtrivial Ġanalysis Ġof Ġnatural Ġlanguage Ġand Ġdomain Ġknowledge . ĠSection Ġ" Exper iments Ġand Ġresults " Ġpresents Ġexperiments Ġand Ġresults , Ġwith Ġa Ġcomparison Ġof Ġthe Ġbaseline Ġmodel Ġwith Ġthe Ġnew Ġone . ĠIn ĠSection Ġ" Related Ġwork " Ġ, Ġwe Ġsurvey Ġrelated Ġwork Ġin Ġthe Ġarea Ġand Ġin ĠSection Ġ" Con clusions " Ġwe Ġconclude Ġthe Ġpaper . </s> Dat as et </s> We Ġconsider Ġthe Ġdataset Ġconsisting Ġof Ġthe Ġentire Ġcollection Ġof Ġarticles Ġof Ġthe ĠWikipedia ĠMedicine ĠPortal , Ġupdated Ġat Ġthe Ġend Ġof Ġ2014 . ĠWikipedia Ġarticles Ġare Ġwritten Ġaccording Ġto Ġthe ĠMedia ĠWiki Ġmarkup Ġlanguage , Ġa ĠHTML - like Ġlanguage . ĠAmong Ġthe Ġstructural Ġelements Ġof Ġone Ġpage , Ġwhich Ġdiffers Ġfrom Ġstandard ĠHTML Ġpages , Ġthere Ġare Ġi ) Ġthe Ġinternal Ġlinks , Ġi . e ., Ġlinks Ġto Ġother ĠWikipedia Ġpages , Ġdifferent Ġfrom Ġlinks Ġto Ġexternal Ġresources ); Ġii ) Ġcategories , Ġwhich Ġrepresent Ġthe ĠMedia ĠWiki Ġcategories Ġa Ġpage Ġbelongs Ġto : Ġthey Ġare Ġencoded Ġin Ġthe Ġpart Ġof Ġtext Ġwithin Ġthe ĠMedia ĠWiki ĠâĢ ľ c ategories " Ġtag Ġin Ġthe Ġpage Ġsource , Ġand Ġiii ) Ġinformative Ġboxes , Ġso Ġcalled ĠâĢ ľ inf ob ox es ", Ġwhich Ġsummarize Ġin Ġa Ġstructured Ġmanner Ġsome Ġpeculiar Ġpieces Ġof Ġinformation Ġrelated Ġthe Ġtopic Ġof Ġthe Ġarticle . ĠThe Ġcategory Ġvalues Ġfor Ġthe Ġarticles Ġin Ġthe Ġmedical Ġportal Ġspan Ġover Ġthe Ġones Ġlisted Ġat Ġhttps :// en . wikipedia . org / wiki / Port al : Medic ine . ĠExamples Ġof Ġcategories , Ġwhich Ġappear Ġat Ġthe Ġbottom Ġof Ġeach ĠWikipedia Ġpage , Ġare Ġin ĠFig . Ġ1 Ġ. </s> Inf ob ox es Ġof Ġthe Ġmedical Ġportal Ġfeature Ġmedical Ġcontent Ġand Ġstandard Ġcoding . ĠAs Ġan Ġexample , ĠFig . Ġ2 Ġshows Ġthe Ġinf ob ox Ġin Ġthe ĠAlzheimer 's Ġdisease Ġpage Ġof Ġthe Ġportal . ĠThe Ġinf ob ox Ġcontains Ġexplanatory Ġfigures Ġand Ġtext Ġden oting Ġpeculiar Ġcharacteristics Ġof Ġthe Ġdisease Ġand Ġthe Ġvalue Ġfor Ġthe Ġstandard Ġcode Ġof Ġsuch Ġdisease Ġ( IC D 9 , Ġas Ġfor Ġthe Ġinternational Ġclassification Ġof Ġthe Ġdisease ). </s> Thanks Ġto ĠWiki Project ĠMedicine , Ġthe Ġdataset Ġof Ġarticles Ġwe Ġcollected Ġfrom Ġthe ĠWikipedia ĠMedicine ĠPortal Ġhas Ġbeen Ġmanually Ġlabeled Ġinto Ġseven Ġquality Ġclasses . ĠThey Ġare Ġordered Ġas ĠStub , ĠStart , ĠC , ĠB , ĠA , ĠGood ĠArticle Ġ( GA ), ĠFeatured ĠArticle Ġ( FA ). ĠThe ĠFeatured Ġand ĠGood Ġarticle Ġclasses Ġare Ġthe Ġhighest Ġones : Ġto Ġhave Ġthose Ġlabels , Ġan Ġarticle Ġrequires Ġa Ġcommunity Ġconsensus Ġand Ġan Ġofficial Ġreview Ġby Ġselected Ġeditors , Ġwhile Ġthe Ġother Ġlabels Ġcan Ġbe Ġachieved Ġwith Ġreviews Ġfrom Ġa Ġlarger , Ġeven Ġcontrolled , Ġset Ġof Ġeditors . ĠActually , Ġnone Ġof Ġthe Ġarticles Ġin Ġthe Ġdataset Ġis Ġlabeled Ġas ĠA , Ġthus , Ġin Ġthe Ġfollowing , Ġwe Ġdo Ġnot Ġconsider Ġthat Ġclass , Ġrestricting Ġthe Ġinvestigation Ġto Ġsix Ġclasses . </s> At Ġthe Ġdate Ġof Ġour Ġstudy , Ġwe Ġwere Ġable Ġto Ġgather Ġ24 , 362 Ġrated Ġdocuments . ĠRem ark ably , Ġonly Ġa Ġsmall Ġpercentage Ġof Ġthem Ġ( 1 %) Ġis Ġlabeled Ġas ĠGA Ġand ĠFA . ĠIndeed , Ġthe Ġdistribution Ġof Ġthe Ġarticles Ġamong Ġthe Ġclasses Ġis Ġhighly Ġskewed . ĠThere Ġare Ġvery Ġfew Ġ( 201 ) Ġarticles Ġfor Ġthe Ġhighest Ġquality Ġclasses Ġ( FA Ġand ĠGA ), Ġwhile Ġthe Ġvast Ġmajority Ġ( 19 , 108 ) Ġbelongs Ġto Ġthe Ġlowest Ġquality Ġones Ġ( St ub Ġand ĠStart ). ĠThis Ġholds Ġnot Ġonly Ġfor Ġthe Ġmedical Ġportal . ĠIndeed , Ġit Ġis Ġcommon Ġin Ġall ĠWikipedia , Ġwhere , Ġon Ġaverage , Ġonly Ġone Ġarticle Ġin Ġevery Ġthousand Ġis Ġa ĠFeatured Ġone . </s> In ĠSection Ġ" Exper iments Ġand Ġresults " Ġ, Ġwe Ġwill Ġadopt Ġa Ġset Ġof Ġmachine - learning Ġclass ifiers Ġto Ġautomatically Ġlabel Ġthe Ġarticles Ġinto Ġthe Ġquality Ġclasses . ĠDe aling Ġwith Ġim balanced Ġclasses Ġis Ġa Ġcommon Ġsituation Ġin Ġmany Ġreal Ġapplications Ġof Ġclassification Ġlearning : Ġhealthy Ġpatients Ġover Ġthe Ġpopulation , Ġfraudulent Ġactions Ġover Ġdaily Ġgenuine Ġtransactions , Ġand Ġso Ġon . ĠWithout Ġany Ġcounter me asure , Ġcommon Ġclass ifiers Ġtend Ġto Ġcorrectly Ġidentify Ġonly Ġarticles Ġbelonging Ġto Ġthe Ġmajority Ġclasses , Ġclearly Ġleading Ġto Ġsevere Ġmis - class ification Ġof Ġthe Ġminority Ġclasses , Ġsince Ġtypical Ġlearning Ġalgorithms Ġstrive Ġto Ġmaximize Ġthe Ġoverall Ġprediction Ġaccuracy . ĠTo Ġreduce Ġthe Ġdise qu ilibrium Ġamong Ġthe Ġsize Ġof Ġthe Ġclasses , Ġwe Ġhave Ġfirst Ġrandomly Ġsampled Ġthe Ġarticles Ġbelonging Ġto Ġthe Ġmost Ġpopulated Ġclasses . ĠThen , Ġwe Ġhave Ġperformed Ġsome Ġfurther Ġelabor ation , Ġas Ġshown Ġin Ġthe Ġfollowing . </s> Many Ġstudies Ġhave Ġbeen Ġconducted Ġto Ġimprove Ġlearning Ġalgorithms Ġaccuracy Ġin Ġpresence Ġof Ġim balanced Ġdata ĠB IB REF 8 Ġ. ĠFor Ġthe Ġcurrent Ġwork , Ġwe Ġhave Ġconsidered Ġone Ġof Ġthe Ġmost Ġpopular Ġapproaches , Ġnamely Ġthe ĠSynt hetic ĠSam pling Ġwith ĠData ĠGeneration , Ġdetailed Ġin ĠB IB REF 9 Ġ. ĠIt Ġconsists Ġin Ġgenerating Ġsynthetic Ġinstances Ġfrom Ġthe Ġminority Ġclasses , Ġto Ġbalance Ġthe Ġoverall Ġdataset . ĠThe Ġapproach Ġhas Ġbeen Ġbroadly Ġapplied Ġto Ġproblems Ġrelying Ġon ĠN LP Ġfeatures , Ġsee , Ġe . g ., ĠB IB REF 10 Ġ. ĠIn Ġour Ġcase , Ġwe Ġres am pled Ġthe Ġinput Ġdata Ġset Ġby Ġapplying Ġthe ĠSynt hetic ĠMinority ĠOvers am pling ĠTE chn ique Ġ( SM OTE ), Ġwith Ġpercentage Ġ40 % Ġfor ĠGA Ġand Ġ180 %, Ġfor ĠFA . ĠIn Ġparticular , Ġthe Ġsteps Ġto Ġovers ample Ġare Ġthe Ġfollowing : </s> Table Ġ1 Ġshows Ġthe Ġnumber Ġof Ġarticles Ġin Ġthe Ġdataset , Ġdivided Ġper Ġclass , Ġas Ġwell Ġas Ġthe Ġrandom Ġsamples Ġwe Ġhave Ġconsidered Ġfor Ġour Ġstudy . ĠThe Ġexperiments Ġpresented Ġin ĠSection Ġ" Exper iments Ġand Ġresults " Ġare Ġbased Ġon Ġthe Ġarticles Ġof Ġthe Ġright - hand Ġcolumn Ġin Ġthe Ġtable . </s> Bas eline : Ġthe Ġaction able Ġmodel </s> We Ġapply Ġa Ġmulti - class Ġclassification Ġapproach Ġto Ġlabel Ġthe Ġarticles Ġof Ġthe Ġsampled Ġdataset Ġinto Ġthe Ġsix ĠWiki Project Ġquality Ġclasses . ĠIn Ġorder Ġto Ġhave Ġa Ġbaseline , Ġwe Ġfirst Ġapply Ġthe Ġstate Ġof Ġthe Ġart Ġmodel Ġproposed Ġin ĠB IB REF 7 Ġto Ġthe Ġdataset . </s> The ĠâĢ ľ action able Ġmodel " Ġin ĠB IB REF 7 Ġfocuses Ġon Ġfive Ġlinguistic Ġand Ġstructural Ġfeatures Ġand Ġit Ġweighs Ġthem Ġas Ġfollows : </s> where </s> In Ġorder Ġto Ġevaluate Ġsuch Ġmodel Ġover Ġour Ġdataset , Ġwe Ġhave Ġextracted Ġfrom Ġour Ġthe Ġdataset Ġthe Ġabove Ġmentioned Ġfeatures . ĠWe Ġhave Ġmeasured ĠArticle Length , ĠNum Wik il inks Ġand ĠNum Bro ken Wik il inks Ġas Ġsuggested Ġin ĠB IB REF 11 Ġ. </s> As Ġshown Ġin ĠFigure Ġ3 Ġ, Ġthe Ġaction able Ġmodel Ġfeatures Ġhave Ġbeen Ġextracted Ġapplying Ġsimple Ġscripts Ġmainly Ġbased Ġon Ġregular Ġexpressions Ġ( the ĠâĢ ľ re gex p Ġextract or " Ġblock ), Ġwhich Ġprocess Ġthe Ġwhole ĠHTML Ġcode Ġof Ġan Ġarticle Ġ( free Ġtext Ġplus Ġmedia Ġwiki Ġtags ). ĠThe Ġregex p Ġextract or Ġrelies Ġon ĠPython ĠBeautiful S oup Ġfor Ġextracting ĠHTML Ġstructures Ġand Ġexcerpts Ġof Ġthe Ġtextual Ġcontent Ġwithin Ġthe ĠMedia Wiki Ġtags Ġand Ġon Ġn lt k Ġlibraries Ġfor Ġbasic ĠN LP Ġanalysis . ĠIn Ġdetails , Ġn lt k Ġhas Ġbeen Ġused Ġfor Ġcomputing Ġthe ĠInfo No ise Ġfeature , Ġwhose Ġcomputation Ġincludes Ġthe Ġstop words Ġremoval , Ġfollowing Ġthe ĠPorter ĠStop words ĠCorpus Ġavailable Ġthrough Ġn lt k ĠB IB REF 12 Ġ. </s> The Ġclassification Ġresults Ġaccording Ġto Ġthe Ġbaseline Ġmodel Ġare Ġreported Ġin ĠSection Ġ" Exper iments Ġand Ġresults " Ġ. </s> The Ġmedical Ġdomain Ġmodel </s> Here , Ġwe Ġimprove Ġthe Ġbaseline Ġmodel Ġwith Ġnovel Ġand Ġspecifically Ġcrafted Ġfeatures Ġthat Ġrely Ġon Ġthe Ġmedical Ġdomain Ġand Ġthat Ġcapture Ġdetails Ġon Ġthe Ġspecific Ġcontent Ġof Ġan Ġarticle . ĠAs Ġshown Ġin ĠFigure Ġ3 Ġ, Ġmedical Ġmodel Ġfeatures , Ġthe Ġbio - medical Ġentities , Ġhave Ġbeen Ġextracted Ġfrom Ġthe Ġfree Ġtext Ġonly , Ġexploiting Ġadvanced ĠN LP Ġtechniques Ġand Ġusing Ġdomain Ġdiction aries . </s> In Ġdetails , Ġwe Ġnewly Ġdefine Ġand Ġextract Ġfrom Ġthe Ġdataset Ġthe Ġfollowing Ġnovel Ġfeatures : </s> The Ġidea Ġof Ġconsidering Ġinf ob ox es Ġis Ġnot Ġnovel : Ġfor Ġexample , Ġin ĠB IB REF 7 Ġthe Ġauthors Ġnoticed Ġthat Ġthe Ġpresence Ġof Ġan Ġinf ob ox Ġis Ġa Ġcharacteristic Ġfeatured Ġby Ġgood Ġarticles . ĠHowever , Ġin Ġthe Ġspecific Ġcase Ġof Ġthe ĠMedicine ĠPortal , Ġthe Ġpresence Ġof Ġan Ġinf ob ox Ġdoes Ġnot Ġseem Ġstrictly Ġrelated Ġto Ġthe Ġquality Ġclass Ġthe Ġarticle Ġbelongs Ġto Ġ( according Ġto Ġthe Ġmanual Ġlab elling ). ĠIndeed , Ġit Ġis Ġrecurrent Ġthat Ġarticles , Ġspanning Ġall Ġclasses , Ġhave Ġan Ġinf ob ox , Ġcontaining Ġa Ġschematic Ġsynthesis Ġof Ġthe Ġarticle . ĠIn Ġparticular , Ġpages Ġwith Ġdescriptions Ġof Ġdiseases Ġusually Ġhave Ġan Ġinf ob ox Ġwith Ġthe Ġmedical Ġstandard Ġcode Ġof Ġthe Ġdisease Ġ( i . e ., ĠID C - 9 Ġand ĠID C - 10 ), Ġas Ġin ĠFigure Ġ2 Ġ. </s> As Ġdone Ġfor Ġthe Ġbaseline , Ġalso Ġthe Ġfirst Ġtwo Ġfeatures Ġof Ġthe Ġmedical Ġmodel Ġhave Ġbeen Ġextracted Ġwith Ġad Ġhoc ĠPython Ġscripts , Ġextracting ĠHTML Ġstructures Ġand Ġexcerpts Ġof Ġthe Ġtextual Ġcontent Ġwithin Ġthe ĠMedia Wiki Ġtags . ĠFor Ġtheir Ġextraction Ġof Ġthe Ġbio - medical Ġentities , Ġwe Ġconsider Ġthe Ġtextual Ġpart Ġof Ġthe Ġarticle Ġonly , Ġobtained Ġafter Ġremoving Ġthe ĠMedia Wiki Ġtags , Ġand Ġwe Ġapply Ġa ĠN LP Ġanalysis , Ġwhich Ġis Ġpresented Ġin ĠSection Ġ" Bio - medical Ġentities " Ġ. </s> Inf ob ox - based Ġfeature </s> We Ġhave Ġcalculated Ġthe ĠInf ob ox Ġsize Ġas Ġthe Ġbase Ġ10 Ġlog Ġof Ġthe Ġbytes Ġof Ġdata Ġcontained Ġwithin Ġthe Ġmedia wiki Ġtags Ġthat Ġwrap Ġan Ġinf ob ox , Ġand Ġwe Ġhave Ġnormalized Ġit Ġwith Ġrespect Ġto Ġthe ĠArticle Length , Ġintroduced Ġin ĠSection Ġ" Bas eline : Ġthe Ġaction able Ġmodel " Ġ. </s> Category - based Ġfeature </s> We Ġhave Ġlever aged Ġthe Ġcategories Ġassigned Ġto Ġarticles Ġin ĠWikipedia , Ġin Ġparticular Ġrelating Ġto Ġthe Ġmedicine Ġtopics Ġavailable Ġat Ġhttps :// en . wikipedia . org / wiki / Port al : Medic ine . </s> We Ġhave Ġdefined Ġ4 Ġupper Ġlevel Ġcategories Ġof Ġour Ġinterest : </s> A Ġanatomy : Ġan Ġarticle Ġis Ġabout Ġanatomy ; </s> B Ġbiography : Ġan Ġarticle Ġis Ġa Ġbiography Ġof Ġsomeone Ġor Ġtell Ġthe Ġhistory Ġof Ġsomething ; </s> D Ġdisorder : Ġit Ġis Ġabout Ġa Ġdisorder ; </s> F Ġfirst Ġaid : Ġit Ġreports Ġinformation Ġfor Ġfirst Ġaid Ġor Ġemergency Ġcontacts ; </s> O Ġother : Ġnone Ġof Ġthe Ġabove . </s> We Ġhave Ġmatched Ġthe Ġarticle 's Ġtext Ġwithin Ġthe ĠMedia Wiki Ġcategories Ġtag Ġwith Ġan Ġapproximate Ġlist Ġof Ġkeywords Ġthat Ġare Ġrelated Ġto Ġour Ġcategory Ġof Ġinterest , Ġas Ġreported Ġin ĠTable Ġ2 Ġ. </s> Bio - medical Ġentities </s> In Ġthe Ġliterature , Ġthere Ġare Ġseveral Ġmethods Ġavailable Ġfor Ġextracting Ġbio - medical Ġentities Ġfrom Ġa Ġtext Ġ( i . e ., Ġfrom Ġmedical Ġnotes Ġand / or Ġarticles ). ĠWe Ġrefer Ġto ĠB IB REF 13 Ġfor Ġan Ġoverview Ġof Ġvaluable Ġexisting Ġtechniques . ĠIn Ġthis Ġwork , Ġwe Ġhave Ġadopted Ġa Ġdictionary - based Ġapproach , Ġwhich Ġexploits Ġlex ical Ġfeatures Ġand Ġdomain Ġknowledge Ġextracted Ġfrom Ġthe ĠUnified ĠMedical ĠLanguages ĠSystem Ġ( U ML S ) ĠMet at hes aurus ĠB IB REF 14 Ġ. ĠThe Ġapproach Ġhas Ġbeen Ġproposed Ġfor Ġthe ĠItalian Ġlanguage Ġin Ġa Ġpast Ġwork ĠB IB REF 15 Ġ. ĠSince Ġthe Ġapproach Ġcombines Ġthe Ġusage Ġof Ġlinguistic Ġanalysis Ġand Ġdomain Ġresources , Ġwe Ġwere Ġable Ġto Ġconveniently Ġadapt Ġit Ġfor Ġthe ĠEnglish Ġlanguage , Ġbeing Ġboth Ġthe Ġlinguistic Ġpipeline Ġand ĠU ML S Ġavailable Ġfor Ġmultiple Ġlanguages Ġ( including ĠEnglish Ġand ĠItalian ). </s> D ictionary - based Ġapproaches Ġhave Ġbeen Ġproved Ġvalid Ġfor Ġthe Ġtask Ġof Ġentities ' Ġextraction , Ġsee , Ġfor Ġexample , Ġanother Ġwell Ġknown , Ġsimilar Ġapproach Ġto Ġthe Ġone Ġadopted Ġhere , Ġi . e ., ĠMet am ap . ĠIt Ġis Ġworth Ġnoting Ġlike , Ġeven Ġthough Ġdictionary - based Ġapproaches Ġcould Ġbe Ġless Ġprecise Ġthan ĠNamed ĠEntity ĠRecogn ition ĠB IB REF 13 Ġ, Ġin Ġour Ġcontext Ġeven Ġan Ġapproximate Ġsolution Ġis Ġenough , Ġsince Ġwe Ġare Ġnot Ġannot ating Ġmedical Ġrecords . ĠInstead , Ġwe Ġare Ġquant ifying Ġthe Ġmole Ġof Ġinherent Ġinformation Ġwithin Ġa Ġtext . </s> Reference Ġdictionary </s> Several Ġont ologies Ġor Ġtax onom ies Ġrelated Ġto Ġthe Ġmedical Ġdomain Ġare Ġavailable Ġin ĠEnglish . ĠTo Ġbuild Ġa Ġmedical Ġdictionary , Ġwe Ġhave Ġextracted Ġdefinitions Ġof Ġmedical Ġentities Ġfrom Ġthe ĠUnified ĠMedical ĠLanguages ĠSystem Ġ( U ML S ) ĠMet at hes aurus ĠB IB REF 14 Ġ. ĠU ML S Ġintegrates Ġbio - medical Ġresources , Ġsuch Ġas ĠSN OM ED - CT Ġthat Ġprovides Ġthe Ġcore Ġterminology Ġfor Ġelectronic Ġhealth Ġrecords . ĠIn Ġaddition , ĠU ML S Ġalso Ġprovides Ġa Ġsemantic Ġnetwork Ġwhere Ġeach Ġentity Ġin Ġthe ĠMet at hes aurus Ġhas Ġan Ġassigned ĠConcept ĠUnique ĠIdent ifier Ġ( C UI ) Ġand Ġit Ġis Ġsem antically Ġtyped . </s> From ĠU ML S , Ġwe Ġhave Ġextracted Ġthe Ġentries Ġbelonging Ġto Ġthe Ġfollowing ĠSN OM ED - CT Ġsemantic Ġgroups : ĠTreatment , ĠSign Ġor ĠSym pt om , ĠDisease Ġor ĠSyndrome , ĠBody ĠParts , ĠOr gans , Ġor ĠOrgan ĠComponents , ĠPath ologic ĠFunction , Ġand ĠMental Ġor ĠBehavioral ĠDys function , Ġfor Ġa Ġtotal Ġof Ġmore Ġthan Ġone Ġmillion Ġentries , Ġas Ġshown Ġin ĠTable Ġ3 Ġ( where Ġthe Ġtwo Ġlast Ġsemantic Ġgroups Ġhave Ġbeen Ġgrouped Ġtogether , Ġunder ĠDisorder ). ĠFurthermore , Ġwe Ġhave Ġextracted Ġcommon ĠDrugs Ġand ĠActive ĠIngredients Ġdefinitions Ġfrom ĠRx Norm , Ġaccessed Ġby ĠRx Term . </s> Starting Ġfrom Ġthe Ġentries Ġin ĠTable Ġ3 Ġ, Ġwe Ġhave Ġalso Ġcomputed Ġapproximate Ġdefinitions , Ġexploiting Ġsynt actic Ġinformation Ġof Ġthe Ġsame Ġentries . ĠIn Ġdetails , Ġwe Ġhave Ġpre - process ed Ġthe Ġentries Ġby Ġmean Ġof Ġthe ĠTan l Ġpipeline ĠB IB REF 16 Ġ, Ġa Ġsuite Ġof Ġmodules Ġfor Ġtext Ġanalytics Ġand ĠN LP , Ġbased Ġon Ġmachine Ġlearning . ĠPre processing Ġhas Ġconsisted Ġin Ġfirst Ġdividing Ġthe Ġentries Ġinto Ġsingle Ġword Ġforms . ĠThen , Ġfor Ġeach Ġform , Ġwe Ġhave Ġidentified Ġthe Ġle mma Ġ( when Ġavailable ) Ġand Ġthe Ġpart Ġof Ġspeech Ġ( POS ). ĠThus , Ġwe Ġhave Ġcreated Ġan Ġapproximate Ġdefinition Ġthat Ġconsists Ġin Ġusing Ġonly Ġthe Ġle mma Ġand Ġcleaning Ġthe Ġtext , Ġexcluding Ġpunct uation , Ġpre pos itions Ġand Ġarticles . ĠAlso , Ġapproximate Ġdefinitions Ġhave Ġbeen Ġnormalized Ġby Ġlower c asing Ġeach Ġword . ĠAs Ġan Ġexample , Ġthe ĠDisorder Ġentry ĠâĢ ľ ane ur ys m Ġof Ġthe Ġvein Ġof Ġgal en " Ġhas Ġbeen Ġstored Ġin Ġthe Ġdictionary , Ġalong Ġwith Ġits Ġapproximate Ġdefinition ĠâĢ ľ ane ur ys m Ġvein Ġgal en ". </s> Ext raction Ġof Ġbio - medical Ġentities </s> We Ġhave Ġextracted Ġthe Ġbio - medical Ġentities Ġpresent Ġin Ġthe ĠWikipedia Ġmedical Ġarticles Ġthrough Ġa Ġn - gram - based Ġtechnique . </s> A Ġpre - processing Ġphase Ġoccurs Ġin Ġa Ġsimilar Ġway Ġas Ġfor Ġthe Ġdictionary Ġcomposition . ĠGiven Ġa ĠWikipedia Ġarticle Ġwritten Ġin ĠEnglish , Ġwe Ġhave Ġpre - process ed Ġthe Ġtextual Ġpart Ġthrough Ġthe ĠTan l Ġpipeline . ĠSimilar Ġto Ġwhat Ġdescribed Ġin ĠSection Ġ" Reference Ġdictionary " Ġfor Ġthe Ġreference Ġdictionary , Ġwe Ġhave Ġfirst Ġdivided Ġthe Ġtext Ġin Ġsentences Ġand Ġthe Ġsentences Ġinto Ġsingle Ġword Ġforms . ĠFor Ġeach Ġform , Ġwe Ġhave Ġconsidered Ġthe Ġle mma Ġ( when Ġavailable ) Ġand Ġthe Ġpart Ġof Ġspeech Ġ( POS ). ĠFor Ġinstance , Ġstarting Ġfrom Ġan Ġexample Ġsentence Ġextracted Ġfrom Ġthe ĠWikipedia Ġpage Ġon Ġthe ĠAlzheimer 's Ġdisease : ĠâĢ ľ Other Ġrisk Ġfactors Ġinclude Ġa Ġhistory Ġof Ġhead Ġinjuries , Ġdepression , Ġor Ġhypertension .", Ġwe Ġhave Ġobtained Ġthe Ġannotation Ġshown Ġin ĠFigure Ġ4 Ġ. </s> As Ġin Ġthe Ġcase Ġof Ġthe Ġdictionary , Ġeach Ġword Ġin Ġthe Ġtext Ġhas Ġbeen Ġlower c asing . </s> After Ġpre - processing Ġthe Ġtext Ġof Ġeach Ġarticle , Ġwe Ġhave Ġattempted Ġto Ġmatch Ġeach Ġn - gram Ġ( with Ġn Ġbetween Ġ1 Ġand Ġ10 ) Ġin Ġthe Ġcorpus Ġwith Ġthe Ġentries Ġin Ġthe Ġextended Ġdictionary . ĠWe Ġboth Ġattempt Ġan Ġexact Ġmatch Ġand Ġan Ġapproximate Ġmatch , Ġthe Ġlatter Ġremoving Ġpre pos itions , Ġpunct uations Ġand Ġarticles Ġfrom Ġthe Ġn - gram s . ĠApp rox imate Ġmatching Ġleads Ġto Ġseveral Ġadvantages . ĠIndeed , Ġexploiting Ġthe Ġtext Ġpre - processing Ġallows Ġto Ġidentify Ġdictionary Ġdefinitions Ġpresent Ġin Ġthe Ġtext , Ġeven Ġwhen Ġthe Ġnumber Ġdiffers . ĠAs Ġan Ġexample , Ġthe Ġdictionary Ġdefinition ĠâĢ ľ in jury " Ġwill Ġmatch Ġwith ĠâĢ ľ in juries ", Ġmentioned Ġin Ġthe Ġtext , Ġbecause Ġin Ġthe Ġapproximation Ġone Ġcan Ġconsider Ġthe Ġle mm as . ĠFurther , Ġconsidering Ġthe ĠPOS Ġallows Ġto Ġidentify Ġmentions Ġwhen Ġinter le aved Ġby Ġpre pos itions , Ġarticles , Ġand Ġconj unctions Ġthat Ġchange Ġthe Ġform Ġbut Ġdo Ġnot Ġalter Ġthe Ġmeaning . ĠAs Ġan Ġexample , Ġthe Ġapproximate Ġdefinition ĠâĢ ľ ane ur ys m Ġvein Ġgal en âĢ Ŀ Ġwill Ġmatch Ġalso Ġwith Ġthe Ġfollowing Ġn - gram : ĠâĢ ľ the Ġan eur ys m Ġand Ġvein Ġof Ġgal en ", Ġif Ġpresent Ġin Ġthe Ġtext . </s> Exper iments Ġand Ġresults </s> In Ġthis Ġsection , Ġwe Ġdescribe Ġthe Ġexperiments Ġand Ġreport Ġthe Ġresults Ġfor Ġthe Ġclassification Ġof ĠWikipedia Ġmedical Ġarticles Ġinto Ġthe Ġsix Ġclasses Ġof Ġthe ĠWikipedia ĠMedicine ĠPortal . ĠWe Ġcompare Ġthe Ġresults Ġobtained Ġadopting Ġfour Ġdifferent Ġclass ifiers : Ġthe Ġaction able Ġmodel Ġin ĠB IB REF 7 Ġand Ġthree Ġclass ifiers Ġthat Ġleverage Ġthe Ġad - h oc Ġfeatures Ġfrom Ġthe Ġmedical Ġdomain Ġdiscussed Ġin Ġthe Ġprevious Ġsections . ĠAll Ġthe Ġexperiments Ġwere Ġrealized Ġwithin Ġthe ĠWe ka Ġframework ĠB IB REF 17 Ġand Ġvalidated Ġthrough Ġ10 Ġfold Ġcross - valid ation . </s> For Ġeach Ġexperiment , Ġwe Ġrelied Ġon Ġthe Ġdataset Ġpresented Ġin ĠSection Ġ" Dat as et " Ġ, Ġand Ġspecifically , Ġon Ġthat Ġobtained Ġafter Ġsampling Ġthe Ġmajority Ġclasses Ġand Ġovers am pling Ġthe Ġminority Ġones Ġ( right - hand Ġcolumn Ġin ĠTable Ġ1 Ġ). ĠThe Ġdataset Ġserves Ġboth Ġas Ġtraining Ġand Ġtest Ġset Ġfor Ġthe Ġclass ifiers . </s> Moreover , Ġto Ġtake Ġinto Ġaccount Ġthe Ġim balanced Ġdata , Ġwe Ġhave Ġapplied Ġseveral Ġclassification Ġalgorithms Ġand , Ġfor Ġthe Ġsake Ġof Ġconc is eness , Ġhereafter Ġwe Ġreport Ġonly Ġthe Ġbest Ġresults Ġwe Ġhave Ġachieved . ĠIn Ġparticular , Ġwe Ġhave Ġexperimented Ġwith Ġbag ging , Ġadaptive Ġboosting Ġand Ġrandom Ġforest Ġand Ġwe Ġreport Ġthe Ġresults Ġfor Ġthe Ġlatter Ġonly . </s> Class ifiers ' Ġfeatures </s> In ĠTable Ġ4 Ġ, Ġwe Ġreport Ġa Ġsummary Ġof Ġthe Ġfeatures Ġfor Ġeach Ġof Ġthe Ġconsidered Ġmodels : Ġthe Ġbaseline Ġmodel Ġin ĠB IB REF 7 Ġand Ġtwo Ġnew Ġmodels Ġthat Ġemploy Ġthe Ġmedical Ġdomain Ġfeatures . ĠIn Ġthe ĠMedical ĠDomain Ġmodel , Ġwe Ġadd Ġto Ġthe Ġbaseline Ġfeatures Ġthe ĠDomain ĠIn format iveness , Ġas Ġdescribed Ġin ĠSection Ġ" The Ġmedical Ġdomain Ġmodel " Ġand Ġ" Bio - medical Ġentities " Ġ. ĠIn Ġaddition , Ġthe ĠFull ĠMedical ĠDomain Ġmodel Ġalso Ġconsiders Ġthe Ġfeatures ĠInfo Box Norm Size Ġand ĠCategory . </s> For Ġeach Ġof Ġthe Ġfeatures , Ġthe Ġtable Ġalso Ġreports Ġthe ĠInformation ĠGain , Ġevaluated Ġon Ġthe Ġwhole Ġdataset Ġ( 24 , 362 Ġarticles ). ĠInformation ĠGain Ġis Ġa Ġwell - known Ġmetric Ġto Ġevaluate Ġthe Ġdependency Ġof Ġone Ġclass Ġfrom Ġa Ġsingle Ġfeature , Ġsee , Ġe . g ., ĠB IB REF 18 Ġ. </s> We Ġcan Ġobserve Ġhow Ġthe ĠDomain ĠIn format iveness Ġfeature Ġhas Ġa Ġconsiderably Ġhigher Ġinf og ain Ġvalue Ġwhen Ġcompared Ġwith ĠIn format iveness . ĠWe Ġanticipate Ġhere Ġthat Ġthis Ġwill Ġlead Ġto Ġa Ġmore Ġaccurate Ġclassification Ġresults Ġfor Ġthe Ġhighest Ġclasses , Ġas Ġreported Ġin Ġthe Ġnext Ġsection . ĠLeading Ġto Ġa Ġgreater Ġaccuracy Ġis Ġalso Ġtrue Ġfor Ġthe Ġother Ġtwo Ġnew Ġfeatures Ġthat , Ġdespite Ġshowing Ġlower Ġvalues Ġof Ġinf og ain , Ġare Ġable Ġto Ġfurther Ġimprove Ġthe Ġclassification Ġresults , Ġmainly Ġfor Ġthe Ġarticles Ġbelonging Ġto Ġthe Ġlowest Ġquality Ġclasses Ġ( St ub Ġand ĠStart ). </s> Class ification Ġresults </s> Table Ġ5 Ġshows Ġthe Ġresults Ġof Ġour Ġmulti - class Ġclassification . ĠFor Ġeach Ġof Ġthe Ġclasses , Ġwe Ġhave Ġcomputed Ġthe ĠR OC ĠArea Ġand ĠF - Measure Ġmetrics ĠB IB REF 19 Ġ. ĠThe Ġlatter , Ġin Ġparticular , Ġis Ġusually Ġconsidered Ġa Ġsignificant Ġmetric Ġin Ġterms Ġof Ġclassification , Ġsince Ġit Ġcombines Ġin Ġone Ġsingle Ġvalue Ġall Ġthe Ġfour Ġindicators Ġthat Ġare Ġgenerally Ġimplied Ġfor Ġevaluating Ġthe Ġclass ifier Ġperformance Ġ( i . e ., Ġnumber Ġof ĠTrue ĠP osit ives , ĠFalse ĠP osit ives , ĠTrue ĠNeg atives Ġand ĠFalse ĠNeg atives ). ĠIn Ġour Ġscenario , Ġthe Ġmeaning Ġof Ġthe Ġindicators , Ġfor Ġeach Ġclass , Ġare Ġas Ġfollows : </s> True ĠP osit ives Ġare Ġthe Ġarticles Ġclassified Ġas Ġbelonging Ġto Ġa Ġcertain Ġclass , Ġthat Ġindeed Ġbelong Ġto Ġthat Ġclass Ġ( according Ġto Ġthe Ġquality Ġratings Ġgiven Ġby Ġthe ĠWiki Project ĠMedicine ); </s> True ĠNeg atives Ġare Ġthe Ġarticles Ġclassified Ġas Ġnot Ġbelonging Ġto Ġa Ġcertain Ġclass , Ġthat Ġindeed Ġdo Ġnot Ġbelong Ġto Ġthat Ġclass ; </s> False ĠP osit ives Ġare Ġthe Ġarticles Ġclassified Ġas Ġbelonging Ġto Ġa Ġcertain Ġclass , Ġthat Ġdo Ġnot Ġbelong Ġto Ġthat Ġclass ; </s> False ĠNeg atives Ġare Ġthe Ġarticles Ġclassified Ġas Ġnot Ġbelonging Ġto Ġa Ġcertain Ġclass , Ġthat Ġinstead Ġbelong Ġto Ġthat Ġclass . </s> At Ġa Ġfirst Ġglance , Ġwe Ġobserve Ġthat , Ġacross Ġall Ġthe Ġmodels , Ġthe Ġarticles Ġwith Ġthe Ġlowest Ġclassification Ġvalues , Ġfor Ġboth ĠR OC Ġand ĠF - Measure , Ġare Ġthose Ġlabeled ĠC Ġand ĠGA . ĠAdding Ġthe ĠDomain ĠIn format iveness Ġfeature Ġproduces Ġa Ġclassification , Ġwhich Ġis Ġslightly Ġworse Ġfor ĠC Ġand ĠFA Ġarticles , Ġbut Ġbetter Ġfor Ġthe Ġother Ġfour Ġclasses . ĠThis Ġis Ġparticularly Ġevident Ġfor Ġthe ĠF - Measure Ġof Ġthe Ġarticles Ġof Ġthe ĠGA Ġclass . ĠA Ġnoticeable Ġmajor Ġimprovement Ġis Ġobtained Ġwith Ġthe Ġintroduction Ġof Ġthe Ġfeatures ĠInfo Box Norm Size Ġand ĠCategory Ġin Ġthe ĠMedical ĠDomain Ġmodel . ĠThe ĠR OC ĠArea Ġincreases Ġfor Ġthe Ġarticles Ġof Ġall Ġthe Ġclasses Ġwithin Ġthe ĠFull ĠMedical ĠDomain , Ġwhile Ġthe ĠF - Measure Ġis Ġalways Ġbetter Ġthan Ġthe ĠBas eline Ġand Ġalmost Ġalways Ġbetter Ġthan Ġthe ĠMedical ĠDomain . </s> The Ġsize Ġof Ġan Ġarticle , Ġexpressed Ġeither Ġas Ġthe Ġword Ġcount , Ġanalyzed Ġin ĠB IB REF 20 Ġ, Ġor Ġas Ġthe Ġarticle Ġlength , Ġas Ġdone Ġhere , Ġappears Ġa Ġvery Ġstrong Ġfeature , Ġable Ġto Ġdiscriminate Ġthe Ġarticles Ġbelonging Ġto Ġthe Ġhighest Ġand Ġlowest Ġquality Ġclasses . ĠThis Ġis Ġtestified Ġalso Ġby Ġthe Ġresults Ġachieved Ġexploiting Ġthe Ġbaseline Ġmodel Ġof ĠB IB REF 7 Ġ, Ġwhich Ġpoorly Ġsucceeds Ġin Ġdiscriminating Ġthe Ġarticles Ġof Ġthe Ġintermediate Ġquality Ġclasses , Ġwhile Ġachieving Ġgood Ġresults Ġfor ĠStub Ġand ĠFA . ĠHere , Ġthe Ġnewly Ġintroduced Ġfeatures Ġhave Ġa Ġpredominant Ġeffect Ġon Ġthe Ġarticles Ġof Ġthe Ġhighest Ġclasses . ĠThis Ġcould Ġbe Ġjustified Ġby Ġthe Ġfact Ġthat Ġthose Ġarticles Ġcontain , Ġon Ġaverage , Ġmore Ġtext Ġand , Ġthen , ĠN LP - based Ġfeatures Ġcan Ġexploit Ġmore Ġwords Ġbelonging Ġto Ġa Ġspecific Ġdomain . </s> Then , Ġwe Ġobserve Ġthat Ġthe ĠR OC ĠArea Ġand Ġthe ĠF - Measure Ġare Ġnot Ġtightly Ġcoupled Ġ( name ly : Ġhigh Ġvalues Ġfor Ġthe Ġfirst Ġmetric Ġcan Ġcorrespond Ġto Ġlow Ġvalues Ġfor Ġthe Ġsecond Ġone , Ġsee Ġfor Ġexample ĠC Ġand ĠGA ): Ġthis Ġis Ġdue Ġto Ġthe Ġnature Ġof Ġthe ĠR OC ĠArea , Ġthat Ġis Ġaffected Ġby Ġthe Ġdifferent Ġsizes Ġof Ġthe Ġconsidered Ġclasses . ĠAs Ġan Ġexample , Ġwe Ġcan Ġobserve Ġthat Ġthe Ġbaseline Ġmodel Ġhas Ġthe Ġsame ĠR OC ĠArea Ġvalue Ġfor Ġthe Ġarticles Ġof Ġboth Ġclass ĠB Ġand Ġclass ĠGA , Ġwhile Ġthe ĠF - Measure Ġof Ġarticles Ġof Ġclass ĠB Ġis Ġ0 . 282 Ġhigher Ġthan Ġthat Ġof Ġclass ĠGA . </s> Finally , Ġthe Ġresults Ġconfirm Ġthat Ġthe Ġadoption Ġof Ġdomain - based Ġfeatures Ġand , Ġin Ġgeneral , Ġof Ġfeatures Ġthat Ġleverage ĠN LP , Ġhelp Ġto Ġdistinguish Ġbetween Ġarticles Ġin Ġthe Ġlowest Ġclasses Ġand Ġarticles Ġin Ġthe Ġhighest Ġclasses , Ġas Ġhighlighted Ġin Ġbold Ġin ĠTable Ġ5 Ġ. ĠWe Ġnotice Ġalso Ġthat Ġexploiting Ġthe Ġfull Ġmedical Ġdomain Ġleads Ġus Ġto Ġthe Ġachievement Ġof Ġthe Ġbest Ġresults . </s> Even Ġif Ġpreliminary , Ġwe Ġbelieve Ġthat Ġthe Ġresults Ġare Ġpromising Ġand Ġcall Ġboth Ġfor Ġfeatures ' Ġfurther Ġrefinement Ġand Ġnovel Ġfeatures , Ġable Ġto Ġdiscriminate Ġamong Ġthe Ġintermediate Ġclasses Ġtoo . </s> Related Ġwork </s> Aut omatic Ġquality Ġevaluation Ġof ĠWikipedia Ġarticles Ġhas Ġbeen Ġaddressed Ġin Ġprevious Ġworks Ġwith Ġboth Ġun super vised Ġand Ġsupervised Ġlearning Ġapproaches . ĠThe Ġcommon Ġidea Ġof Ġmost Ġof Ġthe Ġexisting Ġwork Ġis Ġto Ġidentify Ġa Ġfeature Ġset , Ġhaving Ġas Ġa Ġstarting Ġpoint Ġthe ĠWikipedia Ġproject Ġguidelines , Ġto Ġbe Ġexploited Ġwith Ġthe Ġobjective Ġin Ġmind Ġto Ġdistinguish ĠFeatured ĠArticles . ĠIn ĠB IB REF 21 Ġ, ĠSt vil ia Ġet Ġal . Ġidentify Ġa Ġrelevant Ġset Ġof Ġfeatures , Ġincluding Ġling ual , Ġstructural , Ġhistorical Ġand Ġrep ut ational Ġaspects Ġof Ġeach Ġarticle . ĠThey Ġshow Ġthe Ġeffectiveness Ġof Ġtheir Ġmetrics Ġby Ġapplying Ġboth Ġclust ering Ġand Ġclassification . ĠAs Ġa Ġresult , Ġmore Ġthan Ġ90 % Ġof ĠFA Ġare Ġcorrectly Ġidentified . </s> Bl umen stock ĠB IB REF 20 Ġinspect s Ġthe Ġrelevance Ġof Ġthe Ġword - count Ġfeature Ġat Ġeach Ġquality Ġstage , Ġshowing Ġthat Ġit Ġcan Ġplay Ġa Ġvery Ġimportant Ġrole Ġin Ġthe Ġquality Ġassessment Ġof ĠWikipedia Ġarticles . ĠOnly Ġusing Ġthis Ġfeature , Ġthe Ġauthor Ġachieves Ġa ĠF - me asure Ġof Ġ0 . 9 02 Ġin Ġthe Ġtask Ġof Ġclass ifying Ġfeatured Ġarticles Ġand Ġ0 . 98 3 Ġin Ġthe Ġtask Ġof Ġclass ifying Ġnon Ġfeatured Ġarticles . ĠThe Ġbest Ġresults Ġof Ġthe Ġinvestigation Ġare Ġachieved Ġwith Ġa Ġclass ifier Ġbased Ġon Ġa Ġneural Ġnetwork Ġimplemented Ġwith Ġa Ġmulti - layer Ġpercept ron . </s> In ĠB IB REF 22 Ġ, Ġthe Ġauthors Ġtry Ġto Ġanalyze Ġthe Ġfactors Ġaffecting Ġthe Ġquality Ġof ĠWikipedia Ġarticles , Ġwith Ġrespect Ġto Ġtheir Ġquality Ġclass . ĠThe Ġauthors Ġevaluate Ġa Ġset Ġof Ġ28 Ġfeatures , Ġover Ġa Ġrandom Ġsample Ġof Ġ500 ĠWikipedia Ġarticles , Ġby Ġweighing Ġeach Ġmetric Ġin Ġdifferent Ġstages Ġusing Ġneural Ġnetworks . ĠFind ings Ġare Ġthat Ġlinguistic Ġfeatures Ġweigh Ġmore Ġin Ġthe Ġlowest Ġquality Ġclasses , Ġand Ġstructural Ġfeatures , Ġalong Ġwith Ġhistorical Ġones , Ġbecome Ġmore Ġimportant Ġas Ġthe Ġarticle Ġquality Ġimproves . ĠTheir Ġresults Ġindicate Ġthat Ġthe Ġinformation Ġquality Ġis Ġmainly Ġaffected Ġby Ġcomple teness , Ġand Ġto Ġbe ĠâĢ ľ well - written " Ġis Ġa Ġbasic Ġrequirement Ġin Ġthe Ġinitial Ġstage . ĠInstead , Ġreputation Ġof Ġauthors Ġor Ġeditors Ġis Ġnot Ġso Ġimportant Ġin ĠWikipedia Ġbecause Ġof Ġits Ġhorizontal Ġstructure . ĠIn ĠB IB REF 23 Ġ, Ġthe Ġauthors Ġconsider Ġthe Ġquality Ġof Ġthe Ġdata Ġin Ġthe Ġinf ob ox es Ġof ĠWikipedia , Ġfinding Ġa Ġcorrelation Ġbetween Ġthe Ġquality Ġof Ġinformation Ġin Ġthe Ġinf ob ox Ġand Ġthe Ġarticle Ġitself . </s> In ĠB IB REF 7 Ġ, Ġthe Ġauthors Ġdeal Ġwith Ġthe Ġproblem Ġof Ġdiscriminating Ġbetween Ġtwo Ġlarge Ġclasses , Ġnamely ĠNeed Work , ĠGood Enough Ġ( including Ġin ĠGood Enough Ġboth ĠGA Ġand ĠFA ), Ġin Ġorder Ġto Ġidentify Ġwhich Ġarticles Ġneed Ġfurther Ġrevisions Ġfor Ġbeing Ġfeatured . ĠThey Ġalso Ġintroduce Ġnew Ġcomposite Ġfeatures , Ġthose Ġthat Ġwe Ġhave Ġreferred Ġto Ġas Ġan ĠâĢ ľ action able Ġmodel âĢ Ŀ Ġin ĠSection Ġ" Bas eline : Ġthe Ġaction able Ġmodel " Ġ. ĠThey Ġobtain Ġgood Ġclassification Ġresults , Ġwith Ġa ĠF - me asure Ġof Ġ0 . 8 76 Ġin Ġtheir Ġbest Ġconfiguration . ĠThey Ġalso Ġtry Ġclassification Ġfor Ġall Ġthe Ġseven Ġquality Ġclasses , Ġas Ġdone Ġin Ġthis Ġwork , Ġusing Ġa Ġrandom Ġforest Ġclass ifier Ġwith Ġ100 Ġtrees , Ġwith Ġa Ġreduced Ġset Ġof Ġfeatures . ĠThe Ġpoor Ġresults Ġ( an Ġaverage ĠF - me asure Ġof Ġ0 . 425 ) Ġhighlights Ġthe Ġhardness Ġof Ġthis Ġfine - gr ained Ġclassification . ĠIn Ġthis Ġpaper , Ġwe Ġaddress Ġthis Ġlast Ġtask Ġin Ġa Ġnovel Ġway , Ġby Ġintroducing Ġdomain Ġfeatures , Ġspecially Ġdealing Ġwith Ġthe Ġmedical Ġdomain . ĠThe Ġresults Ġof Ġthe Ġinvestigation Ġare Ġpromising . </s> Recent Ġstudies Ġspecifically Ġaddress Ġthe Ġquality Ġof Ġmedical Ġinformation Ġ( in ĠWikipedia Ġas Ġwell Ġas Ġin Ġother Ġresources ): Ġin ĠB IB REF 24 Ġand ĠB IB REF 25 Ġ, Ġthe Ġauthors Ġdebate Ġif ĠWikipedia Ġis Ġa Ġreliable Ġlearning Ġresource Ġfor Ġmedical Ġstudents , Ġevaluating Ġarticles Ġon Ġrespiratory Ġtopics Ġand Ġcardiovascular Ġdiseases . ĠThe Ġevaluation Ġis Ġcarried Ġout Ġby Ġexploiting ĠDIS C ERN , Ġa Ġtool Ġevaluating Ġread ability Ġof Ġarticles . ĠIn ĠB IB REF 26 Ġthe Ġauthors Ġprovide Ġnovel Ġsolutions Ġfor Ġmeasure Ġthe Ġquality Ġof Ġmedical Ġinformation Ġin ĠWikipedia , Ġby Ġadopting Ġan Ġun super vised Ġapproach Ġbased Ġon Ġthe ĠAnaly tic ĠHier achy ĠProcess , Ġa Ġmulti - crit eria Ġdecision Ġmaking Ġtechnique ĠB IB REF 27 Ġ. ĠThe Ġwork Ġin ĠB IB REF 28 Ġaims Ġto Ġprovide Ġthe Ġweb Ġsurf ers Ġa Ġnumerical Ġindication Ġof ĠQuality Ġof ĠMedical ĠWeb ĠSites . ĠIn Ġparticular Ġin ĠB IB REF 28 Ġthe Ġauthor Ġproposes Ġan Ġindex Ġto Ġmake ĠIQ Ġjudgment Ġof Ġthe Ġcontent Ġand Ġof Ġits Ġreliability , Ġto Ġgive Ġthe Ġso Ġcalled ĠâĢ ľ surface Ġmarkers âĢ Ŀ Ġand ĠâĢ ľ trust Ġindicator âĢ Ŀ . ĠA Ġsimilar Ġmeasurement Ġis Ġconsidered Ġin ĠB IB REF 29 Ġ. Ġwhere Ġthe Ġauthors Ġpresent Ġan Ġempirical Ġanalysis Ġthat Ġsuggests Ġthe Ġneed Ġto Ġdefine Ġgenre - specific Ġtemplates Ġfor Ġquality Ġevaluation Ġand Ġto Ġdevelop Ġmodels Ġfor Ġan Ġautomatic Ġgenre - based Ġclassification Ġof Ġhealth Ġinformation ĠWeb Ġpages . ĠIn Ġaddition , Ġthe Ġstudy Ġshows Ġthat Ġconsumers Ġmay Ġlack Ġthe Ġmotivation Ġor Ġliteracy Ġskills Ġto Ġevaluate Ġthe Ġinformation Ġquality Ġof Ġhealth ĠWeb Ġpages . ĠClearly , Ġthis Ġfurther Ġhighlights Ġthe Ġcrucial ity Ġto Ġdevelop Ġaccessible Ġautomatic Ġinformation Ġquality Ġevaluation Ġtools Ġand Ġont ologies . ĠOur Ġwork Ġmoves Ġtowards Ġthe Ġgoal , Ġby Ġspecifically Ġconsidering Ġdomain - relevant Ġfeatures Ġand Ġfeaturing Ġan Ġautomatic Ġclassification Ġtask Ġspanning Ġover Ġmore Ġthan Ġtwo Ġclasses . </s> Con clusions </s> In Ġthis Ġwork , Ġwe Ġaimed Ġto Ġprovide Ġa Ġfine Ġgr ained Ġclassification Ġmechanism Ġfor Ġall Ġthe Ġquality Ġclasses Ġof Ġthe Ġarticles Ġof Ġthe ĠWikipedia ĠMedical ĠPortal . ĠThe Ġidea Ġwas Ġto Ġpropose Ġan Ġautomatic Ġinstrument Ġfor Ġhelping Ġthe Ġreviewers Ġto Ġunderstand Ġwhich Ġarticles Ġare Ġthe Ġless Ġwork - dem anding Ġpapers Ġto Ġpass Ġto Ġnext Ġquality Ġstage . ĠWe Ġfocused Ġon Ġan Ġaction able Ġmodel , Ġnamely Ġwhose Ġfeatures Ġare Ġrelated Ġto Ġthe Ġcontent Ġof Ġthe Ġarticles , Ġso Ġthat Ġthey Ġcan Ġalso Ġdirectly Ġsuggest Ġstrategies Ġfor Ġimproving Ġa Ġgiven Ġarticle . ĠAn Ġimportant Ġand Ġnovel Ġaspect Ġof Ġour Ġclass ifier , Ġwith Ġrespect Ġto Ġprevious Ġworks , Ġis Ġthe Ġleveraging Ġof Ġfeatures Ġextracted Ġfrom Ġthe Ġspecific , Ġmedical Ġdomain , Ġwith Ġthe Ġhelp Ġof ĠNatural ĠLanguage ĠProcessing Ġtechniques . ĠAs Ġthe Ġresults Ġof Ġour Ġexperiments Ġconfirm , Ġconsidering Ġspecific Ġdomain - based Ġfeatures , Ġlike ĠDomain ĠIn format iveness Ġand ĠCategory , Ġcan Ġeventually Ġhelp Ġand Ġimprove Ġthe Ġautomatic Ġclassification Ġresults . ĠSince Ġthe Ġresults Ġare Ġencouraging , Ġas Ġfuture Ġwork Ġwe Ġwill Ġevaluate Ġother Ġfeatures Ġbased Ġon Ġthe Ġspecific Ġmedical Ġdomain . ĠMoreover , Ġwe Ġare Ġplanning Ġto Ġextend Ġour Ġidea , Ġto Ġinclude Ġand Ġcompare Ġalso Ġother Ġnon Ġmedical Ġarticles Ġ( thus , Ġextending Ġthe Ġwork Ġto Ġinclude Ġother Ġdomains ), Ġin Ġorder Ġto Ġfurther Ġvalidate Ġour Ġapproach . </s> Acknowled gments . ĠThe Ġresearch Ġleading Ġto Ġthese Ġresults Ġhas Ġbeen Ġpartially Ġfunded Ġby Ġthe ĠRegist ro . it Ġproject ĠMy ĠInformation ĠBubble ĠM IB .