Named Entity Recognition
EXTRACTIVE
Named Entity Recognition, including entities such as proteins, genes, diseases, treatments, drugs, etc. in the biomedical domain
ABSTRACTIVE
Named Entity Recognition (NER) in the Biomedical domain usually includes recognition of entities such as proteins, genes, diseases, treatments, drugs, etc. Fact extraction involves extraction of Named Entities from a corpus, usually given a certain ontology. When compared to NER in the domain of general text, the biomedical domain has some characteristic challenges:
Named Entity Recognition (NER) in the Biomedical domain usually includes recognition of entities such as proteins, genes, diseases, treatments, drugs, etc. Fact extraction involves extraction of Named Entities from a corpus, usually given a certain ontology. When compared to NER in the domain of general text, the biomedical domain has some characteristic challenges:
1705.05437
Introduction </s> The Ġexplosion Ġof Ġavailable Ġscientific Ġarticles Ġin Ġthe ĠBi omedical Ġdomain Ġhas Ġled Ġto Ġthe Ġrise Ġof ĠBi omedical ĠInformation ĠExt raction Ġ( Bio IE ). ĠBio IE Ġsystems Ġaim Ġto Ġextract Ġinformation Ġfrom Ġa Ġwide Ġspectrum Ġof Ġarticles Ġincluding Ġmedical Ġliterature , Ġbiological Ġliterature , Ġelectronic Ġhealth Ġrecords , Ġetc . Ġthat Ġcan Ġbe Ġused Ġby Ġclinicians Ġand Ġresearchers Ġin Ġthe Ġfield . ĠOften Ġthe Ġoutputs Ġof ĠBio IE Ġsystems Ġare Ġused Ġto Ġassist Ġin Ġthe Ġcreation Ġof Ġdatabases , Ġor Ġto Ġsuggest Ġnew Ġpaths Ġfor Ġresearch . ĠFor Ġexample , Ġa Ġranked Ġlist Ġof Ġinteracting Ġproteins Ġthat Ġare Ġextracted Ġfrom Ġbiomedical Ġliterature , Ġbut Ġare Ġnot Ġpresent Ġin Ġexisting Ġdatabases , Ġcan Ġallow Ġresearchers Ġto Ġmake Ġinformed Ġdecisions Ġabout Ġwhich Ġprotein / g ene Ġto Ġstudy Ġfurther . ĠInter actions Ġbetween Ġdrugs Ġare Ġnecessary Ġfor Ġclinicians Ġwho Ġsimultaneously Ġadminister Ġmultiple Ġdrugs Ġto Ġtheir Ġpatients . ĠA Ġdatabase Ġof Ġdiseases , Ġtreatments Ġand Ġtests Ġis Ġbeneficial Ġfor Ġdoctors Ġconsulting Ġin Ġcomplicated Ġmedical Ġcases . </s> The Ġmain Ġproblems Ġin ĠBio IE Ġare Ġsimilar Ġto Ġthose Ġin ĠInformation ĠExt raction : </s> This Ġpaper Ġdiscusses , Ġin Ġeach Ġsection , Ġvarious Ġmethods Ġthat Ġhave Ġbeen Ġadopted Ġto Ġsolve Ġthe Ġlisted Ġproblems . ĠEach Ġsection Ġalso Ġhighlights Ġthe Ġdifficulty Ġof ĠInformation ĠExt raction Ġtasks Ġin Ġthe Ġbiomedical Ġdomain . </s> This Ġpaper Ġis Ġintended Ġas Ġa Ġprimer Ġto ĠBi omedical ĠInformation ĠExt raction Ġfor Ġcurrent ĠN LP Ġresearchers . ĠIt Ġaims Ġto Ġhighlight Ġthe Ġdiversity Ġof Ġthe Ġvarious Ġtechniques Ġfrom ĠInformation ĠExt raction Ġthat Ġhave Ġbeen Ġapplied Ġin Ġthe ĠBi omedical Ġdomain . ĠThe Ġstate Ġof Ġbiomedical Ġtext Ġmining Ġis Ġreviewed Ġregularly . ĠFor Ġmore Ġextensive Ġsurveys , Ġconsult ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ. </s> N amed ĠEntity ĠRecogn ition Ġand ĠFact ĠExt raction </s> N amed ĠEntity ĠRecogn ition Ġ( NER ) Ġin Ġthe ĠBi omedical Ġdomain Ġusually Ġincludes Ġrecognition Ġof Ġentities Ġsuch Ġas Ġproteins , Ġgenes , Ġdiseases , Ġtreatments , Ġdrugs , Ġetc . ĠFact Ġextraction Ġinvolves Ġextraction Ġof ĠNamed ĠEnt ities Ġfrom Ġa Ġcorpus , Ġusually Ġgiven Ġa Ġcertain Ġont ology . ĠWhen Ġcompared Ġto ĠN ER Ġin Ġthe Ġdomain Ġof Ġgeneral Ġtext , Ġthe Ġbiomedical Ġdomain Ġhas Ġsome Ġcharacteristic Ġchallenges : </s> Some Ġof Ġthe Ġearliest Ġsystems Ġwere Ġheavily Ġdependent Ġon Ġhand - crafted Ġfeatures . ĠThe Ġmethod Ġproposed Ġin ĠB IB REF 4 Ġfor Ġrecognition Ġof Ġprotein Ġnames Ġin Ġtext Ġdoes Ġnot Ġrequire Ġany Ġprepared Ġdictionary . ĠThe Ġwork Ġgives Ġexamples Ġof Ġdiversity Ġin Ġprotein Ġnames Ġand Ġlists Ġmultiple Ġrules Ġdepending Ġon Ġsimple Ġword Ġfeatures Ġas Ġwell Ġas ĠPOS Ġtags . </s> BI B REF 5 Ġadopt Ġa Ġmachine Ġlearning Ġapproach Ġfor ĠN ER . ĠTheir ĠN ER Ġsystem Ġextracts Ġmedical Ġproblems , Ġtests Ġand Ġtreatments Ġfrom Ġdischarge Ġsumm aries Ġand Ġprogress Ġnotes . ĠThey Ġuse Ġa Ġsemi - Cond itional ĠRandom ĠField Ġ( se mi - CR F ) ĠB IB REF 6 Ġto Ġoutput Ġlabels Ġover Ġall Ġtokens Ġin Ġthe Ġsentence . ĠThey Ġuse Ġa Ġvariety Ġof Ġtoken , Ġcontext Ġand Ġsentence Ġlevel Ġfeatures . ĠThey Ġalso Ġuse Ġsome Ġconcept Ġmapping Ġfeatures Ġusing Ġexisting Ġannotation Ġtools , Ġas Ġwell Ġas ĠBrown Ġclust ering Ġto Ġform Ġ128 Ġclusters Ġover Ġthe Ġunl ab elled Ġdata . ĠThe Ġdataset Ġused Ġis Ġthe Ġi 2 b 2 Ġ2010 Ġchallenge Ġdataset . ĠTheir Ġsystem Ġachieves Ġan ĠF - Score Ġof Ġ0 . 85 . ĠB IB REF 7 Ġis Ġan Ġincremental Ġpaper Ġon ĠN ER Ġtag gers . ĠIt Ġuses Ġ3 Ġtypes Ġof Ġword - represent ation Ġtechniques Ġ( Brown Ġclust ering , Ġdistribution al Ġclust ering , Ġword Ġvectors ) Ġto Ġimprove Ġperformance Ġof Ġthe ĠN ER ĠCond itional ĠRandom ĠField Ġtag ger , Ġand Ġachieves Ġmarginal ĠF - Score Ġimprovements . </s> BI B REF 8 Ġpropose Ġa Ġboost ra pping Ġmechanism Ġto Ġboot strap Ġbiomedical Ġont ologies Ġusing ĠNE LL ĠB IB REF 9 Ġ, Ġwhich Ġuses Ġa Ġcoupled Ġsemi - super vised Ġboot stra pping Ġapproach Ġto Ġextract Ġfacts Ġfrom Ġtext , Ġgiven Ġan Ġont ology Ġand Ġa Ġsmall Ġnumber Ġof ĠâĢ ľ seed âĢ Ŀ Ġexamples Ġfor Ġeach Ġcategory . ĠThis Ġinteresting Ġapproach Ġ( called ĠBio NE LL ) Ġuses Ġan Ġont ology Ġof Ġover Ġ100 Ġcategories . ĠIn Ġcontrast Ġto ĠNE LL , ĠBio NE LL Ġdoes Ġnot Ġcontain Ġany Ġrelations Ġin Ġthe Ġont ology . ĠBio NE LL Ġis Ġmotivated Ġby Ġthe Ġfact Ġthat Ġa Ġlot Ġof Ġscientific Ġliterature Ġavailable Ġonline Ġis Ġhighly Ġreliable Ġdue Ġto Ġpeer - review . ĠThe Ġauthors Ġnote Ġthat Ġthe Ġalgorithm Ġused Ġby ĠNE LL Ġto Ġboot strap Ġfails Ġin ĠBio NE LL Ġdue Ġto Ġambig u ities Ġin Ġbiomedical Ġliterature , Ġand Ġheavy Ġsemantic Ġdrift . ĠOne Ġof Ġthe Ġcauses Ġfor Ġthis Ġis Ġthat Ġoften Ġcommon Ġwords Ġsuch Ġas ĠâĢ ľ white âĢ Ŀ , ĠâĢ ľ dad âĢ Ŀ , ĠâĢ ľ arm âĢ Ŀ Ġare Ġused Ġas Ġnames Ġof Ġgenes - Ġthis Ġcan Ġeasily Ġresult Ġin Ġsemantic Ġdrift Ġin Ġone Ġiteration Ġof Ġthe Ġboot stra pping . ĠIn Ġorder Ġto Ġmitigate Ġthis , Ġthey Ġuse ĠPoint wise ĠMutual ĠInformation Ġscores Ġfor Ġcorpus Ġlevel Ġstatistics , Ġwhich Ġattributes Ġa Ġsmall Ġscore Ġto Ġcommon Ġwords . ĠIn Ġaddition , Ġin Ġcontrast Ġto ĠNE LL , ĠBio NE LL Ġonly Ġuses Ġhigh Ġinstances Ġas Ġseeds Ġin Ġthe Ġnext Ġiteration , Ġbut Ġadds Ġlow Ġranking Ġinstances Ġto Ġthe Ġknowledge Ġbase . ĠSince Ġevaluation Ġis Ġnot Ġpossible Ġusing ĠMechanical ĠTurk Ġor Ġa Ġsmall Ġnumber Ġof Ġexperts Ġ( due Ġto Ġthe Ġcomplexity Ġof Ġthe Ġtask ), Ġthey Ġuse ĠFree base ĠB IB REF 10 Ġ, Ġa Ġknowledge Ġbase Ġthat Ġhas Ġsome Ġbiomedical Ġconcepts Ġas Ġwell . ĠThe Ġlex icon Ġlearned Ġusing ĠBio NE LL Ġis Ġused Ġto Ġtrain Ġan ĠN ER Ġsystem . ĠThe Ġsystem Ġshows Ġa Ġvery Ġhigh Ġprecision , Ġthereby Ġshowing Ġthat ĠBio NE LL Ġlearns Ġvery Ġfew Ġambiguous Ġterms . </s> More Ġrecently , Ġdeep Ġlearning Ġtechniques Ġhave Ġbeen Ġdeveloped Ġto Ġfurther Ġenhance Ġthe Ġperformance Ġof ĠN ER Ġsystems . ĠB IB REF 11 Ġexplore Ġrecurrent Ġneural Ġnetworks Ġfor Ġthe Ġproblem Ġof ĠN ER Ġin Ġbiomedical Ġtext . </s> Rel ation ĠExt raction </s> In ĠBi omedical ĠInformation ĠExt raction , ĠRel ation ĠExt raction Ġinvolves Ġfinding Ġrelated Ġentities Ġof Ġmany Ġdifferent Ġkinds . ĠSome Ġof Ġthese Ġinclude Ġprotein - protein Ġinteractions , Ġdisease - g ene Ġrelations Ġand Ġdrug - drug Ġinteractions . ĠDue Ġto Ġthe Ġexplosion Ġof Ġavailable Ġbiomedical Ġliterature , Ġit Ġis Ġimpossible Ġfor Ġone Ġperson Ġto Ġextract Ġrelevant Ġrelations Ġfrom Ġpublished Ġmaterial . ĠAutomatic Ġextraction Ġof Ġrelations Ġassists Ġin Ġthe Ġprocess Ġof Ġdatabase Ġcreation , Ġby Ġsuggesting Ġpotentially Ġrelated Ġentities Ġwith Ġlinks Ġto Ġthe Ġsource Ġarticle . ĠFor Ġexample , Ġa Ġdatabase Ġof Ġdrug - drug Ġinteractions Ġis Ġimportant Ġfor Ġclinicians Ġwho Ġadminister Ġmultiple Ġdrugs Ġsimultaneously Ġto Ġtheir Ġpatients - Ġit Ġis Ġimperative Ġto Ġknow Ġif Ġone Ġdrug Ġwill Ġhave Ġan Ġadverse Ġeffect Ġon Ġthe Ġother . ĠA Ġvariety Ġof Ġmethods Ġhave Ġbeen Ġdeveloped Ġfor Ġrelation Ġextract ions , Ġand Ġare Ġoften Ġinspired Ġby ĠRel ation ĠExt raction Ġin ĠN LP Ġtasks . ĠThese Ġinclude Ġrule - based Ġapproaches , Ġhand - crafted Ġpatterns , Ġfeature - based Ġand Ġkernel Ġmachine Ġlearning Ġmethods , Ġand Ġmore Ġrecently Ġdeep Ġlearning Ġarchitectures . ĠRel ation ĠExt raction Ġsystems Ġover ĠBi omedical ĠCorpor a Ġare Ġoften Ġaffected Ġby Ġnoisy Ġextraction Ġof Ġentities , Ġdue Ġto Ġambig u ities Ġin Ġnames Ġof Ġproteins , Ġgenes , Ġdrugs Ġetc . </s> BI B REF 12 Ġwas Ġone Ġof Ġthe Ġfirst Ġlarge Ġscale ĠInformation ĠExt raction Ġefforts Ġto Ġstudy Ġthe Ġfeasibility Ġof Ġextraction Ġof Ġprotein - protein Ġinteractions Ġ( such Ġas ĠâĢ ľ protein ĠA Ġactivates Ġprotein ĠB ") Ġfrom ĠBi omedical Ġtext . ĠUsing Ġ8 Ġhand - crafted Ġregular Ġexpressions Ġover Ġa Ġfixed Ġvocabulary , Ġthe Ġauthors Ġwere Ġable Ġto Ġachieve Ġa Ġrecall Ġof Ġ30 % Ġfor Ġinteractions Ġpresent Ġin ĠThe ĠDictionary Ġof ĠInter acting ĠPro te ins Ġ( D IP ) Ġfrom Ġabstract s Ġin ĠMedline . ĠThe Ġmethod Ġdid Ġnot Ġdifferentiate Ġbetween Ġthe Ġtype Ġof Ġrelation . ĠThe Ġreasons Ġfor Ġthe Ġlow Ġrecall Ġwere Ġthe Ġinconsistency Ġin Ġprotein Ġn omen cl ature , Ġinformation Ġnot Ġpresent Ġin Ġthe Ġabstract , Ġand Ġdue Ġto Ġspecificity Ġof Ġthe Ġhand - crafted Ġpatterns . ĠOn Ġa Ġsmall Ġsubset Ġof Ġextracted Ġrelations , Ġthey Ġfound Ġthat Ġabout Ġ60 % Ġwere Ġtrue Ġinteractions Ġbetween Ġproteins Ġnot Ġpresent Ġin ĠD IP . </s> BI B REF 13 Ġcombine Ġsentence Ġlevel Ġrelation Ġextraction Ġfor Ġprotein Ġinteractions Ġwith Ġcorpus Ġlevel Ġstatistics . ĠSimilar Ġto ĠB IB REF 12 Ġ, Ġthey Ġdo Ġnot Ġconsider Ġthe Ġtype Ġof Ġinteraction Ġbetween Ġproteins - Ġonly Ġwhether Ġthey Ġinteract Ġin Ġthe Ġgeneral Ġsense Ġof Ġthe Ġword . ĠThey Ġalso Ġdo Ġnot Ġdifferentiate Ġbetween Ġgenes Ġand Ġtheir Ġprotein Ġproducts Ġ( which Ġmay Ġshare Ġthe Ġsame Ġname ). ĠThey Ġuse ĠPoint wise ĠMutual ĠInformation Ġ( PM I ) Ġfor Ġcorpus Ġlevel Ġstatistics Ġto Ġdetermine Ġwhether Ġa Ġpair Ġof Ġproteins Ġoccur Ġtogether Ġby Ġchance Ġor Ġbecause Ġthey Ġinteract . ĠThey Ġcombine Ġthis Ġwith Ġa Ġconfidence Ġaggreg ator Ġthat Ġtakes Ġthe Ġmaximum Ġof Ġthe Ġconfidence Ġof Ġthe Ġextract or Ġover Ġall Ġextract ions Ġfor Ġthe Ġsame Ġprotein - pair . ĠThe Ġextraction Ġuses Ġa Ġsubsequ ence Ġkernel Ġbased Ġon ĠB IB REF 14 Ġ. ĠThe Ġintegrated Ġmodel , Ġthat Ġcombines ĠPM I Ġwith Ġaggregate Ġconfidence , Ġgives Ġthe Ġbest Ġperformance . ĠKernel Ġmethods Ġhave Ġwidely Ġbeen Ġstudied Ġfor ĠRel ation ĠExt raction Ġin ĠBi omedical ĠLiterature . ĠCommon Ġkernels Ġused Ġusually Ġexploit Ġlinguistic Ġinformation Ġby Ġutil ising Ġkernels Ġbased Ġon Ġthe Ġdependency Ġtree ĠB IB REF 15 Ġ, ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ. </s> BI B REF 18 Ġlook Ġat Ġthe Ġextraction Ġof Ġdiseases Ġand Ġtheir Ġrelevant Ġgenes . ĠThey Ġuse Ġa Ġdictionary Ġfrom Ġsix Ġpublic Ġdatabases Ġto Ġannot ate Ġgenes Ġand Ġdiseases Ġin ĠMedline Ġabstract s . ĠIn Ġtheir Ġwork , Ġthe Ġauthors Ġnote Ġthat Ġwhen Ġboth Ġgenes Ġand Ġdiseases Ġare Ġcorrectly Ġidentified , Ġthey Ġare Ġrelated Ġin Ġ94 % Ġof Ġthe Ġcases . ĠThe Ġproblem Ġthen Ġreduces Ġto Ġfiltering Ġincorrect Ġmatches Ġusing Ġthe Ġdictionary , Ġwhich Ġoccurs Ġdue Ġto Ġfalse Ġpositives Ġresulting Ġfrom Ġambig u ities Ġin Ġthe Ġnames Ġas Ġwell Ġas Ġambig u ities Ġin Ġabbrevi ations . ĠTo Ġthis Ġend , Ġthey Ġtrain Ġa ĠMax - Ent Ġbased ĠN ER Ġclass ifier Ġfor Ġthe Ġtask , Ġand Ġget Ġa Ġ26 % Ġgain Ġin Ġprecision Ġover Ġthe Ġunf il tered Ġbaseline , Ġwith Ġa Ġslight Ġhit Ġin Ġrecall . ĠThey Ġuse ĠPOS Ġtags , Ġexpanded Ġforms Ġof Ġabbrevi ations , Ġindicators Ġfor ĠGreek Ġletters Ġas Ġwell Ġas Ġsuffix es Ġand Ġprefix es Ġcommonly Ġused Ġin Ġbiomedical Ġterms . </s> BI B REF 19 Ġadopt Ġa Ġsupervised Ġfeature - based Ġapproach Ġfor Ġthe Ġextraction Ġof Ġdrug - drug Ġinteraction Ġ( DD I ) Ġfor Ġthe ĠD DI - 2013 Ġdataset ĠB IB REF 20 Ġ. ĠThey Ġpartition Ġthe Ġdata Ġin Ġsubs ets Ġdepending Ġon Ġthe Ġsynt actic Ġfeatures , Ġand Ġtrain Ġa Ġdifferent Ġmodel Ġfor Ġeach . ĠThey Ġuse Ġlex ical , Ġsynt actic Ġand Ġverb Ġbased Ġfeatures Ġon Ġtop Ġof Ġshallow Ġparse Ġfeatures , Ġin Ġaddition Ġto Ġa Ġhand - crafted Ġlist Ġof Ġtrigger Ġwords Ġto Ġdefine Ġtheir Ġfeatures . ĠAn ĠS VM Ġclass ifier Ġis Ġthen Ġtrained Ġon Ġthe Ġfeature Ġvectors , Ġwith Ġa Ġpositive Ġlabel Ġif Ġthe Ġdrug Ġpair Ġinteracts , Ġand Ġnegative Ġotherwise . ĠTheir Ġmethod Ġbeats Ġother Ġsystems Ġon Ġthe ĠD DI - 2013 Ġdataset . ĠSome Ġother Ġfeature - based Ġapproaches Ġare Ġdescribed Ġin ĠB IB REF 21 Ġ, ĠB IB REF 22 Ġ. </s> D istant Ġsupervision Ġmethods Ġhave Ġalso Ġbeen Ġapplied Ġto Ġrelation Ġextraction Ġover Ġbiomedical Ġcorpor a . ĠIn ĠB IB REF 23 Ġ, Ġ10 , 000 Ġneuroscience Ġarticles Ġare Ġdist antly Ġsupervised Ġusing Ġinformation Ġfrom ĠU ML S ĠSem antic ĠNetwork Ġto Ġclassify Ġbrain - g ene Ġrelations Ġinto Ġgene Exp ression Ġand Ġother Rel ation . ĠThey Ġuse Ġlex ical Ġ( bag Ġof Ġwords , Ġcontextual ) Ġfeatures Ġas Ġwell Ġas Ġsynt actic Ġ( depend ency Ġparse Ġfeatures ). ĠThey Ġmake Ġthe ĠâĢ ľ at - le ast Ġone âĢ Ŀ Ġassumption , Ġi . e . Ġat Ġleast Ġone Ġof Ġthe Ġsentences Ġextracted Ġfor Ġa Ġgiven Ġentity - pair Ġcontains Ġthe Ġrelation Ġin Ġdatabase . ĠThey Ġmodel Ġit Ġas Ġa Ġmulti - instance Ġlearning Ġproblem Ġand Ġadopt Ġa Ġgraphical Ġmodel Ġsimilar Ġto ĠB IB REF 24 Ġ. ĠThey Ġtest Ġusing Ġmanually Ġannot ated Ġexamples . ĠThey Ġnote Ġthat Ġthe ĠF - score Ġachieved Ġare Ġmuch Ġlesser Ġthan Ġthat Ġachieved Ġin Ġthe Ġgeneral Ġdomain Ġin ĠB IB REF 24 Ġ, Ġand Ġattribute Ġto Ġgenerally Ġpoorer Ġperformance Ġof ĠN ER Ġtools Ġin Ġthe Ġbiomedical Ġdomain , Ġas Ġwell Ġas Ġless Ġtraining Ġexamples . ĠB IB REF 25 Ġexplore Ġdistant Ġsupervision Ġmethods Ġfor Ġprotein - protein Ġinteraction Ġextraction . </s> More Ġrecently , Ġdeep Ġlearning Ġmethods Ġhave Ġbeen Ġapplied Ġto Ġrelation Ġextraction Ġin Ġthe Ġbiomedical Ġdomain . ĠOne Ġof Ġthe Ġmain Ġadvantages Ġof Ġsuch Ġmethods Ġover Ġtraditional Ġfeature Ġor Ġkernel Ġbased Ġlearning Ġmethods Ġis Ġthat Ġthey Ġrequire Ġminimal Ġfeature Ġengineering . ĠIn ĠB IB REF 26 Ġ, Ġskip - gram Ġvectors ĠB IB REF 27 Ġare Ġtrained Ġover Ġ5 . 6 Gb Ġof Ġunl ab elled Ġtext . ĠThey Ġuse Ġthese Ġvectors Ġto Ġextract Ġprotein - protein Ġinteractions Ġby Ġconverting Ġthem Ġinto Ġfeatures Ġfor Ġentities , Ġcontext Ġand Ġthe Ġentire Ġsentence . ĠUsing Ġan ĠS VM Ġfor Ġclassification , Ġtheir Ġmethod Ġis Ġable Ġto Ġoutper form Ġmany Ġkernel Ġand Ġfeature Ġbased Ġmethods Ġover Ġa Ġvariety Ġof Ġdatasets . </s> BI B REF 28 Ġfollow Ġa Ġsimilar Ġmethod Ġby Ġusing Ġword Ġvectors Ġtrained Ġon ĠPubMed Ġarticles . ĠThey Ġuse Ġit Ġfor Ġthe Ġtask Ġof Ġrelation Ġextraction Ġfrom Ġclinical Ġtext Ġfor Ġentities Ġthat Ġinclude Ġproblem , Ġtreatment Ġand Ġmedical Ġtest . ĠFor Ġa Ġgiven Ġsentence , Ġgiven Ġlabelled Ġentities , Ġthey Ġpredict Ġthe Ġtype Ġof Ġrelation Ġexhibited Ġ( or ĠNone ) Ġby Ġthe Ġentity Ġpair . ĠThese Ġtypes Ġinclude ĠâĢ ľ treatment Ġcaused Ġmedical Ġproblem âĢ Ŀ , ĠâĢ ľ test Ġconducted Ġto Ġinvestigate Ġmedical Ġproblem âĢ Ŀ , ĠâĢ ľ medical Ġproblem Ġindicates Ġmedical Ġproblems âĢ Ŀ , Ġetc . ĠThey Ġuse Ġa ĠConv olution al ĠNeural ĠNetwork Ġ( CNN ) Ġfollowed Ġby Ġfeed forward Ġneural Ġnetwork Ġarchitecture Ġfor Ġprediction . ĠIn Ġaddition Ġto Ġpre - trained Ġword Ġvectors Ġas Ġfeatures , Ġfor Ġeach Ġtoken Ġthey Ġalso Ġadd Ġfeatures Ġfor ĠPOS Ġtags , Ġdistance Ġfrom Ġboth Ġthe Ġentities Ġin Ġthe Ġsentence , Ġas Ġwell ĠB IO Ġtags Ġfor Ġthe Ġentities . ĠTheir Ġmodel Ġperforms Ġbetter Ġthan Ġa Ġfeature Ġbased ĠS VM Ġbaseline Ġthat Ġthey Ġtrain Ġthemselves . </s> The ĠBio N LP ' 16 ĠShared ĠT asks Ġhas Ġalso Ġintroduced Ġsome ĠRel ation ĠExt raction Ġtasks , Ġin Ġparticular Ġthe ĠBB 3 - event Ġsubt ask Ġthat Ġinvolves Ġpredicting Ġwhether Ġa ĠâĢ ľ l ives - in âĢ Ŀ Ġrelation Ġholds Ġfor Ġa ĠB acteria Ġin Ġa Ġlocation . ĠSome Ġof Ġthe Ġtop Ġperforming Ġmodels Ġfor Ġthis Ġtask Ġare Ġdeep Ġlearning Ġmodels . ĠB IB REF 29 Ġtrain Ġword Ġembed d ings Ġwith Ġsix Ġbillions Ġwords Ġof Ġscientific Ġtexts Ġfrom ĠPubMed . ĠThey Ġthen Ġconsider Ġthe Ġshortest Ġdependency Ġpath Ġbetween Ġthe Ġtwo Ġentities Ġ( B acteria Ġand Ġlocation ). ĠFor Ġeach Ġtoken Ġin Ġthe Ġpath , Ġthey Ġuse Ġword Ġembed ding Ġfeatures , ĠPOS Ġtype Ġembed d ings Ġand Ġdependency Ġtype Ġembed d ings . ĠThey Ġtrain Ġa Ġun id irection al ĠL ST M ĠB IB REF 30 Ġover Ġthe Ġdependency Ġpath , Ġthat Ġachieves Ġan ĠF - Score Ġof Ġ52 . 1 % Ġon Ġthe Ġtest Ġset . </s> BI B REF 31 Ġimprove Ġthe Ġperformance Ġby Ġmaking Ġmodifications Ġto Ġthe Ġabove Ġmodel . ĠInstead Ġof Ġusing Ġthe Ġshortest Ġdependency Ġpath , Ġthey Ġmodify Ġthe Ġparse Ġtree Ġbased Ġon Ġsome Ġpr uning Ġstrategies . ĠThey Ġalso Ġadd Ġfeature Ġembed d ings Ġfor Ġeach Ġtoken Ġto Ġrepresent Ġthe Ġdistance Ġfrom Ġthe Ġentities Ġin Ġthe Ġshortest Ġpath . ĠThey Ġthen Ġtrain Ġa ĠBid irection al ĠL ST M Ġon Ġthe Ġpath , Ġand Ġobtain Ġan ĠF - Score Ġof Ġ57 . 1 %. </s> The Ġrecent Ġsuccess Ġof Ġdeep Ġlearning Ġmodels Ġin ĠBi omedical ĠRel ation ĠExt raction Ġthat Ġrequire Ġminimal Ġfeature Ġengineering Ġis Ġpromising . ĠThis Ġalso Ġsuggests Ġnew Ġavenues Ġof Ġresearch Ġin Ġthe Ġfield . ĠAn Ġapproach Ġas Ġin ĠB IB REF 32 Ġcan Ġbe Ġused Ġto Ġcombine Ġmulti - instance Ġlearning Ġand Ġdistant Ġsupervision Ġwith Ġa Ġneural Ġarchitecture . </s> Event ĠExt raction </s> Event ĠExt raction Ġin Ġthe ĠBi omedical Ġdomain Ġis Ġa Ġtask Ġthat Ġhas Ġgained Ġmore Ġimportance Ġrecently . ĠEvent ĠExt raction Ġgoes Ġbeyond ĠRel ation ĠExt raction . ĠIn ĠBi omedical ĠEvent ĠExt raction , Ġevents Ġgenerally Ġrefer Ġto Ġa Ġchange Ġin Ġthe Ġstate Ġof Ġbiological Ġmolecules Ġsuch Ġas Ġproteins Ġand ĠDNA . ĠGenerally , Ġit Ġincludes Ġdetection Ġof Ġtargeted Ġevent Ġtypes Ġsuch Ġas Ġgene Ġexpression , Ġregulation , Ġlocal isation Ġand Ġtranscription . ĠEach Ġevent Ġtype Ġin Ġaddition Ġcan Ġhave Ġmultiple Ġarguments Ġthat Ġneed Ġto Ġbe Ġdetected . ĠAn Ġadditional Ġlayer Ġof Ġcomplexity Ġcomes Ġfrom Ġthe Ġfact Ġthat Ġevents Ġcan Ġalso Ġbe Ġarguments Ġof Ġother Ġevents , Ġgiving Ġrise Ġto Ġa Ġnested Ġstructure . ĠThis Ġhelps Ġto Ġcapture Ġthe Ġunderlying Ġbiology Ġbetter ĠB IB REF 1 Ġ. ĠDetect ing Ġthe Ġevent Ġtype Ġoften Ġinvolves Ġrecogn ising Ġand Ġclass ifying Ġtrigger Ġwords . ĠOften , Ġthese Ġwords Ġare Ġverbs Ġsuch Ġas ĠâĢ ľ activ ates âĢ Ŀ , ĠâĢ ľ in hib its âĢ Ŀ , ĠâĢ ľ ph osph ory lation âĢ Ŀ Ġthat Ġmay Ġindicate Ġa Ġsingle , Ġor Ġsometimes Ġmultiple Ġevent Ġtypes . ĠIn Ġthis Ġsection , Ġwe Ġwill Ġdiscuss Ġsome Ġof Ġthe Ġsuccessful Ġmodels Ġfor ĠEvent ĠExt raction Ġin Ġsome Ġdetail . </s> Event ĠExt raction Ġgained Ġa Ġlot Ġof Ġinterest Ġwith Ġthe Ġavailability Ġof Ġan Ġannot ated Ġcorpus Ġwith Ġthe ĠBio N LP ' 09 ĠShared ĠTask Ġon ĠEvent ĠExt raction ĠB IB REF 34 Ġ. ĠThe Ġtask Ġinvolves Ġprediction Ġof Ġtrigger Ġwords Ġover Ġnine Ġevent Ġtypes Ġsuch Ġas Ġexpression , Ġtranscription , Ġcat abol ism , Ġbinding , Ġetc . Ġgiven Ġonly Ġannotation Ġof Ġnamed Ġentities Ġ( pro te ins , Ġgenes , Ġetc .). ĠFor Ġeach Ġevent , Ġits Ġclass , Ġtrigger Ġexpression Ġand Ġarguments Ġneed Ġto Ġbe Ġextracted . ĠSince Ġthe Ġevents Ġcan Ġbe Ġarguments Ġto Ġother Ġevents , Ġthe Ġfinal Ġoutput Ġin Ġgeneral Ġis Ġa Ġgraph Ġrepresentation Ġwith Ġevents Ġand Ġnamed Ġentities Ġas Ġnodes , Ġand Ġedges Ġthat Ġcorrespond Ġto Ġevent Ġarguments . ĠB IB REF 33 Ġpresent Ġa Ġpipeline Ġbased Ġmethod Ġthat Ġis Ġheavily Ġdependent Ġon Ġdependency Ġparsing . ĠTheir Ġpipeline Ġapproach Ġconsists Ġof Ġthree Ġsteps : Ġtrigger Ġdetection , Ġargument Ġdetection Ġand Ġsemantic Ġpost - processing . ĠWhile Ġthe Ġfirst Ġtwo Ġcomponents Ġare Ġlearning Ġbased Ġsystems , Ġthe Ġlast Ġcomponent Ġis Ġa Ġrule Ġbased Ġsystem . ĠFor Ġthe ĠBio N LP ' 09 Ġcorpus , Ġonly Ġ5 % Ġof Ġthe Ġevents Ġspan Ġmultiple Ġsentences . ĠHence Ġthe Ġapproach Ġdoes Ġnot Ġget Ġaffected Ġseverely Ġby Ġconsidering Ġonly Ġsingle Ġsentences . ĠIt Ġis Ġimportant Ġto Ġnote Ġthat Ġtrigger Ġwords Ġcannot Ġsimply Ġbe Ġreduced Ġto Ġa Ġdictionary Ġlookup . ĠThis Ġis Ġbecause Ġa Ġspecific Ġword Ġmay Ġbelong Ġto Ġmultiple Ġclasses , Ġor Ġmay Ġnot Ġalways Ġbe Ġa Ġtrigger Ġword Ġfor Ġan Ġevent . ĠFor Ġexample , ĠâĢ ľ activate âĢ Ŀ Ġis Ġfound Ġto Ġnot Ġbe Ġa Ġtrigger Ġword Ġin Ġover Ġ70 % Ġof Ġthe Ġcases . ĠA Ġmulti - class ĠS VM Ġis Ġtrained Ġfor Ġtrigger Ġdetection Ġon Ġeach Ġtoken , Ġusing Ġa Ġlarge Ġfeature Ġset Ġconsisting Ġof Ġsemantic Ġand Ġsynt actic Ġfeatures . ĠIt Ġis Ġinteresting Ġto Ġnote Ġthat Ġthe Ġhyper param eters Ġof Ġthis Ġclass ifier Ġare Ġoptim ised Ġbased Ġon Ġthe Ġperformance Ġof Ġthe Ġentire Ġend - to - end Ġsystem . </s> For Ġthe Ġsecond Ġcomponent Ġto Ġdetect Ġarguments , Ġlabels Ġfor Ġedges Ġbetween Ġentities Ġmust Ġbe Ġpredicted . ĠFor Ġthe ĠBio N LP ' 09 ĠShared ĠTask , Ġeach Ġdirected Ġedge Ġfrom Ġone Ġevent Ġnode Ġto Ġanother Ġevent Ġnode , Ġor Ġfrom Ġan Ġevent Ġnode Ġto Ġa Ġnamed Ġentity Ġnode Ġare Ġclassified Ġas ĠâĢ ľ theme âĢ Ŀ , ĠâĢ ľ cause âĢ Ŀ , Ġor ĠNone . ĠThe Ġsecond Ġcomponent Ġof Ġthe Ġpipeline Ġmakes Ġthese Ġpredictions Ġindependently . ĠThis Ġis Ġalso Ġtrained Ġusing Ġa Ġmulti - class ĠS VM Ġwhich Ġinvolves Ġheavy Ġuse Ġof Ġsynt actic Ġfeatures , Ġincluding Ġthe Ġshortest Ġdependency Ġpath Ġbetween Ġthe Ġnodes . ĠThe Ġauthors Ġnote Ġthat Ġthe Ġprecision - rec all Ġchoice Ġof Ġthe Ġfirst Ġcomponent Ġaffects Ġthe Ġperformance Ġof Ġthe Ġsecond Ġcomponent : Ġsince Ġthe Ġsecond Ġcomponent Ġis Ġonly Ġtrained Ġon ĠGold Ġexamples , Ġany Ġerror Ġby Ġthe Ġfirst Ġcomponent Ġwill Ġlead Ġto Ġa Ġcasc ading Ġof Ġerrors . ĠThe Ġfinal Ġcomponent , Ġwhich Ġis Ġa Ġsemantic Ġpost - processing Ġstep , Ġconsists Ġof Ġrules Ġand Ġhe ur istics Ġto Ġcorrect Ġthe Ġoutput Ġof Ġthe Ġsecond Ġcomponent . ĠSince Ġthe Ġedge Ġpredictions Ġare Ġmade Ġindependently , Ġit Ġis Ġpossible Ġthat Ġsome Ġevent Ġnodes Ġdo Ġnot Ġhave Ġany Ġedges , Ġor Ġhave Ġan Ġimproper Ġcombination Ġof Ġedges . ĠThe Ġrule Ġbased Ġcomponent Ġcorrect s Ġthese Ġand Ġapplies Ġrules Ġto Ġbreak Ġdirected Ġcycles Ġin Ġthe Ġgraph , Ġand Ġsome Ġspecific Ġhe ur istics Ġfor Ġdifferent Ġtypes Ġof Ġevents . ĠThe Ġfinal Ġmodel Ġgives Ġa Ġcumulative ĠF - Score Ġof Ġ52 % Ġon Ġthe Ġtest Ġset , Ġand Ġwas Ġthe Ġbest Ġmodel Ġon Ġthe Ġtask . </s> BI B REF 35 Ġnote Ġthat Ġprevious Ġapproaches Ġon Ġthe Ġtask Ġsuffer Ġdue Ġto Ġthe Ġpipeline Ġnature Ġand Ġthe Ġpropagation Ġof Ġerrors . ĠTo Ġcounter Ġthis , Ġthey Ġadopt Ġa Ġjoint Ġinference Ġmethod Ġbased Ġon ĠMark ov ĠLogic ĠNetworks ĠB IB REF 36 Ġfor Ġthe Ġsame Ġtask Ġon ĠBio N LP ' 09 . ĠThe ĠMark ov ĠLogic ĠNetwork Ġjointly Ġpredicts Ġwhether Ġeach Ġtoken Ġis Ġa Ġtrigger Ġword , Ġand Ġif Ġyes , Ġthe Ġclass Ġit Ġbelongs Ġto ; Ġfor Ġeach Ġdependency Ġedge , Ġwhether Ġit Ġis Ġan Ġargument Ġpath Ġleading Ġto Ġa ĠâĢ ľ theme âĢ Ŀ Ġor Ġa ĠâĢ ľ cause âĢ Ŀ . ĠBy Ġform ulating Ġthe ĠEvent ĠExt raction Ġproblem Ġusing Ġan ĠML N , Ġthe Ġapproach Ġbecomes Ġcomput ationally Ġfeasible Ġand Ġonly Ġlinear Ġin Ġthe Ġlength Ġof Ġthe Ġsentence . ĠThey Ġincorporate Ġhard Ġconstraints Ġto Ġencode Ġrules Ġsuch Ġas ĠâĢ ľ an Ġargument Ġpath Ġmust Ġhave Ġan Ġevent âĢ Ŀ , ĠâĢ ľ a Ġcause Ġpath Ġmust Ġstart Ġwith Ġa Ġregulation Ġevent âĢ Ŀ , Ġetc . ĠIn Ġaddition , Ġthey Ġalso Ġinclude Ġsome Ġdomain Ġspecific Ġsoft Ġconstraints Ġas Ġwell Ġas Ġsome Ġlingu istically - mot ivated Ġcontext - specific Ġsoft Ġconstraints . ĠIn Ġorder Ġto Ġtrain Ġthe ĠML N , Ġsto ch astic Ġgradient Ġdescent Ġwas Ġused . ĠCertain Ġhe uristic Ġmethods Ġare Ġimplemented Ġin Ġorder Ġto Ġdeal Ġwith Ġerrors Ġdue Ġto Ġsynt actic Ġparsing , Ġespecially Ġambig u ities Ġin ĠPP - att achment Ġand Ġcoordination . ĠTheir Ġfinal Ġsystem Ġis Ġcompetitive Ġand Ġcomes Ġvery Ġclose Ġto Ġthe Ġsystem Ġby ĠB IB REF 33 Ġwith Ġan Ġaverage ĠF - Score Ġof Ġ50 %. ĠTo Ġfurther Ġimprove Ġthe Ġsystem , Ġthey Ġsuggest Ġleveraging Ġadditional Ġjoint - in ference Ġopportunities Ġand Ġintegrating Ġthe Ġsynt actic Ġparser Ġbetter . ĠSome Ġother Ġmore Ġrecent Ġmodels Ġfor ĠBi omedical ĠEvent ĠExt raction Ġinclude ĠB IB REF 37 Ġ, ĠB IB REF 38 Ġ. </s> Conclusion </s> We Ġhave Ġdiscussed Ġsome Ġof Ġthe Ġmajor Ġproblems Ġand Ġchallenges Ġin ĠBio IE , Ġand Ġseen Ġsome Ġof Ġthe Ġdiverse Ġapproaches Ġadopted Ġto Ġsolve Ġthem . ĠSome Ġinteresting Ġproblems Ġsuch Ġas ĠPath way ĠExt raction Ġfor ĠBiological ĠSystems ĠB IB REF 39 Ġ, ĠB IB REF 40 Ġhave Ġnot Ġbeen Ġdiscussed . </s> Bi omedical ĠInformation ĠExt raction Ġis Ġa Ġchallenging Ġand Ġexciting Ġfield Ġfor ĠN LP Ġresearchers Ġthat Ġdemands Ġapplication Ġof Ġstate - of - the - art Ġmethods . ĠTrad itionally , Ġthere Ġhas Ġbeen Ġa Ġdependence Ġon Ġhand - crafted Ġfeatures Ġor Ġheavily Ġfeature - engine ered Ġmethods . ĠHowever , Ġwith Ġthe Ġadvent Ġof Ġdeep Ġlearning Ġmethods , Ġa Ġlot Ġof ĠBio IE Ġtasks Ġare Ġseeing Ġan Ġimprovement Ġby Ġadopting Ġdeep Ġlearning Ġmodels Ġsuch Ġas ĠConv olution al ĠNeural ĠNetworks Ġand ĠL ST Ms , Ġwhich Ġrequire Ġminimal Ġfeature Ġengineering . ĠRapid Ġprogress Ġin Ġdeveloping Ġbetter Ġsystems Ġfor ĠBio IE Ġwill Ġbe Ġextremely Ġhelpful Ġfor Ġclinicians Ġand Ġresearchers Ġin Ġthe ĠBi omedical Ġdomain .
Introduction The explosion of available scientific articles in the Biomedical domain has led to the rise of Biomedical Information Extraction (BioIE). BioIE systems aim to extract information from a wide spectrum of articles including medical literature, biological literature, electronic health records, etc. that can be used by clinicians and researchers in the field. Often the outputs of BioIE systems are used to assist in the creation of databases, or to suggest new paths for research. For example, a ranked list of interacting proteins that are extracted from biomedical literature, but are not present in existing databases, can allow researchers to make informed decisions about which protein/gene to study further. Interactions between drugs are necessary for clinicians who simultaneously administer multiple drugs to their patients. A database of diseases, treatments and tests is beneficial for doctors consulting in complicated medical cases. The main problems in BioIE are similar to those in Information Extraction: This paper discusses, in each section, various methods that have been adopted to solve the listed problems. Each section also highlights the difficulty of Information Extraction tasks in the biomedical domain. This paper is intended as a primer to Biomedical Information Extraction for current NLP researchers. It aims to highlight the diversity of the various techniques from Information Extraction that have been applied in the Biomedical domain. The state of biomedical text mining is reviewed regularly. For more extensive surveys, consult BIBREF0 , BIBREF1 , BIBREF2 . Named Entity Recognition and Fact Extraction Named Entity Recognition (NER) in the Biomedical domain usually includes recognition of entities such as proteins, genes, diseases, treatments, drugs, etc. Fact extraction involves extraction of Named Entities from a corpus, usually given a certain ontology. When compared to NER in the domain of general text, the biomedical domain has some characteristic challenges: Some of the earliest systems were heavily dependent on hand-crafted features. The method proposed in BIBREF4 for recognition of protein names in text does not require any prepared dictionary. The work gives examples of diversity in protein names and lists multiple rules depending on simple word features as well as POS tags. BIBREF5 adopt a machine learning approach for NER. Their NER system extracts medical problems, tests and treatments from discharge summaries and progress notes. They use a semi-Conditional Random Field (semi-CRF) BIBREF6 to output labels over all tokens in the sentence. They use a variety of token, context and sentence level features. They also use some concept mapping features using existing annotation tools, as well as Brown clustering to form 128 clusters over the unlabelled data. The dataset used is the i2b2 2010 challenge dataset. Their system achieves an F-Score of 0.85. BIBREF7 is an incremental paper on NER taggers. It uses 3 types of word-representation techniques (Brown clustering, distributional clustering, word vectors) to improve performance of the NER Conditional Random Field tagger, and achieves marginal F-Score improvements. BIBREF8 propose a boostrapping mechanism to bootstrap biomedical ontologies using NELL BIBREF9 , which uses a coupled semi-supervised bootstrapping approach to extract facts from text, given an ontology and a small number of “seed” examples for each category. This interesting approach (called BioNELL) uses an ontology of over 100 categories. In contrast to NELL, BioNELL does not contain any relations in the ontology. BioNELL is motivated by the fact that a lot of scientific literature available online is highly reliable due to peer-review. The authors note that the algorithm used by NELL to bootstrap fails in BioNELL due to ambiguities in biomedical literature, and heavy semantic drift. One of the causes for this is that often common words such as “white”, “dad”, “arm” are used as names of genes- this can easily result in semantic drift in one iteration of the bootstrapping. In order to mitigate this, they use Pointwise Mutual Information scores for corpus level statistics, which attributes a small score to common words. In addition, in contrast to NELL, BioNELL only uses high instances as seeds in the next iteration, but adds low ranking instances to the knowledge base. Since evaluation is not possible using Mechanical Turk or a small number of experts (due to the complexity of the task), they use Freebase BIBREF10 , a knowledge base that has some biomedical concepts as well. The lexicon learned using BioNELL is used to train an NER system. The system shows a very high precision, thereby showing that BioNELL learns very few ambiguous terms. More recently, deep learning techniques have been developed to further enhance the performance of NER systems. BIBREF11 explore recurrent neural networks for the problem of NER in biomedical text. Relation Extraction In Biomedical Information Extraction, Relation Extraction involves finding related entities of many different kinds. Some of these include protein-protein interactions, disease-gene relations and drug-drug interactions. Due to the explosion of available biomedical literature, it is impossible for one person to extract relevant relations from published material. Automatic extraction of relations assists in the process of database creation, by suggesting potentially related entities with links to the source article. For example, a database of drug-drug interactions is important for clinicians who administer multiple drugs simultaneously to their patients- it is imperative to know if one drug will have an adverse effect on the other. A variety of methods have been developed for relation extractions, and are often inspired by Relation Extraction in NLP tasks. These include rule-based approaches, hand-crafted patterns, feature-based and kernel machine learning methods, and more recently deep learning architectures. Relation Extraction systems over Biomedical Corpora are often affected by noisy extraction of entities, due to ambiguities in names of proteins, genes, drugs etc. BIBREF12 was one of the first large scale Information Extraction efforts to study the feasibility of extraction of protein-protein interactions (such as “protein A activates protein B") from Biomedical text. Using 8 hand-crafted regular expressions over a fixed vocabulary, the authors were able to achieve a recall of 30% for interactions present in The Dictionary of Interacting Proteins (DIP) from abstracts in Medline. The method did not differentiate between the type of relation. The reasons for the low recall were the inconsistency in protein nomenclature, information not present in the abstract, and due to specificity of the hand-crafted patterns. On a small subset of extracted relations, they found that about 60% were true interactions between proteins not present in DIP. BIBREF13 combine sentence level relation extraction for protein interactions with corpus level statistics. Similar to BIBREF12 , they do not consider the type of interaction between proteins- only whether they interact in the general sense of the word. They also do not differentiate between genes and their protein products (which may share the same name). They use Pointwise Mutual Information (PMI) for corpus level statistics to determine whether a pair of proteins occur together by chance or because they interact. They combine this with a confidence aggregator that takes the maximum of the confidence of the extractor over all extractions for the same protein-pair. The extraction uses a subsequence kernel based on BIBREF14 . The integrated model, that combines PMI with aggregate confidence, gives the best performance. Kernel methods have widely been studied for Relation Extraction in Biomedical Literature. Common kernels used usually exploit linguistic information by utilising kernels based on the dependency tree BIBREF15 , BIBREF16 , BIBREF17 . BIBREF18 look at the extraction of diseases and their relevant genes. They use a dictionary from six public databases to annotate genes and diseases in Medline abstracts. In their work, the authors note that when both genes and diseases are correctly identified, they are related in 94% of the cases. The problem then reduces to filtering incorrect matches using the dictionary, which occurs due to false positives resulting from ambiguities in the names as well as ambiguities in abbreviations. To this end, they train a Max-Ent based NER classifier for the task, and get a 26% gain in precision over the unfiltered baseline, with a slight hit in recall. They use POS tags, expanded forms of abbreviations, indicators for Greek letters as well as suffixes and prefixes commonly used in biomedical terms. BIBREF19 adopt a supervised feature-based approach for the extraction of drug-drug interaction (DDI) for the DDI-2013 dataset BIBREF20 . They partition the data in subsets depending on the syntactic features, and train a different model for each. They use lexical, syntactic and verb based features on top of shallow parse features, in addition to a hand-crafted list of trigger words to define their features. An SVM classifier is then trained on the feature vectors, with a positive label if the drug pair interacts, and negative otherwise. Their method beats other systems on the DDI-2013 dataset. Some other feature-based approaches are described in BIBREF21 , BIBREF22 . Distant supervision methods have also been applied to relation extraction over biomedical corpora. In BIBREF23 , 10,000 neuroscience articles are distantly supervised using information from UMLS Semantic Network to classify brain-gene relations into geneExpression and otherRelation. They use lexical (bag of words, contextual) features as well as syntactic (dependency parse features). They make the “at-least one” assumption, i.e. at least one of the sentences extracted for a given entity-pair contains the relation in database. They model it as a multi-instance learning problem and adopt a graphical model similar to BIBREF24 . They test using manually annotated examples. They note that the F-score achieved are much lesser than that achieved in the general domain in BIBREF24 , and attribute to generally poorer performance of NER tools in the biomedical domain, as well as less training examples. BIBREF25 explore distant supervision methods for protein-protein interaction extraction. More recently, deep learning methods have been applied to relation extraction in the biomedical domain. One of the main advantages of such methods over traditional feature or kernel based learning methods is that they require minimal feature engineering. In BIBREF26 , skip-gram vectors BIBREF27 are trained over 5.6Gb of unlabelled text. They use these vectors to extract protein-protein interactions by converting them into features for entities, context and the entire sentence. Using an SVM for classification, their method is able to outperform many kernel and feature based methods over a variety of datasets. BIBREF28 follow a similar method by using word vectors trained on PubMed articles. They use it for the task of relation extraction from clinical text for entities that include problem, treatment and medical test. For a given sentence, given labelled entities, they predict the type of relation exhibited (or None) by the entity pair. These types include “treatment caused medical problem”, “test conducted to investigate medical problem”, “medical problem indicates medical problems”, etc. They use a Convolutional Neural Network (CNN) followed by feedforward neural network architecture for prediction. In addition to pre-trained word vectors as features, for each token they also add features for POS tags, distance from both the entities in the sentence, as well BIO tags for the entities. Their model performs better than a feature based SVM baseline that they train themselves. The BioNLP'16 Shared Tasks has also introduced some Relation Extraction tasks, in particular the BB3-event subtask that involves predicting whether a “lives-in” relation holds for a Bacteria in a location. Some of the top performing models for this task are deep learning models. BIBREF29 train word embeddings with six billions words of scientific texts from PubMed. They then consider the shortest dependency path between the two entities (Bacteria and location). For each token in the path, they use word embedding features, POS type embeddings and dependency type embeddings. They train a unidirectional LSTM BIBREF30 over the dependency path, that achieves an F-Score of 52.1% on the test set. BIBREF31 improve the performance by making modifications to the above model. Instead of using the shortest dependency path, they modify the parse tree based on some pruning strategies. They also add feature embeddings for each token to represent the distance from the entities in the shortest path. They then train a Bidirectional LSTM on the path, and obtain an F-Score of 57.1%. The recent success of deep learning models in Biomedical Relation Extraction that require minimal feature engineering is promising. This also suggests new avenues of research in the field. An approach as in BIBREF32 can be used to combine multi-instance learning and distant supervision with a neural architecture. Event Extraction Event Extraction in the Biomedical domain is a task that has gained more importance recently. Event Extraction goes beyond Relation Extraction. In Biomedical Event Extraction, events generally refer to a change in the state of biological molecules such as proteins and DNA. Generally, it includes detection of targeted event types such as gene expression, regulation, localisation and transcription. Each event type in addition can have multiple arguments that need to be detected. An additional layer of complexity comes from the fact that events can also be arguments of other events, giving rise to a nested structure. This helps to capture the underlying biology better BIBREF1 . Detecting the event type often involves recognising and classifying trigger words. Often, these words are verbs such as “activates”, “inhibits”, “phosphorylation” that may indicate a single, or sometimes multiple event types. In this section, we will discuss some of the successful models for Event Extraction in some detail. Event Extraction gained a lot of interest with the availability of an annotated corpus with the BioNLP'09 Shared Task on Event Extraction BIBREF34 . The task involves prediction of trigger words over nine event types such as expression, transcription, catabolism, binding, etc. given only annotation of named entities (proteins, genes, etc.). For each event, its class, trigger expression and arguments need to be extracted. Since the events can be arguments to other events, the final output in general is a graph representation with events and named entities as nodes, and edges that correspond to event arguments. BIBREF33 present a pipeline based method that is heavily dependent on dependency parsing. Their pipeline approach consists of three steps: trigger detection, argument detection and semantic post-processing. While the first two components are learning based systems, the last component is a rule based system. For the BioNLP'09 corpus, only 5% of the events span multiple sentences. Hence the approach does not get affected severely by considering only single sentences. It is important to note that trigger words cannot simply be reduced to a dictionary lookup. This is because a specific word may belong to multiple classes, or may not always be a trigger word for an event. For example, “activate” is found to not be a trigger word in over 70% of the cases. A multi-class SVM is trained for trigger detection on each token, using a large feature set consisting of semantic and syntactic features. It is interesting to note that the hyperparameters of this classifier are optimised based on the performance of the entire end-to-end system. For the second component to detect arguments, labels for edges between entities must be predicted. For the BioNLP'09 Shared Task, each directed edge from one event node to another event node, or from an event node to a named entity node are classified as “theme”, “cause”, or None. The second component of the pipeline makes these predictions independently. This is also trained using a multi-class SVM which involves heavy use of syntactic features, including the shortest dependency path between the nodes. The authors note that the precision-recall choice of the first component affects the performance of the second component: since the second component is only trained on Gold examples, any error by the first component will lead to a cascading of errors. The final component, which is a semantic post-processing step, consists of rules and heuristics to correct the output of the second component. Since the edge predictions are made independently, it is possible that some event nodes do not have any edges, or have an improper combination of edges. The rule based component corrects these and applies rules to break directed cycles in the graph, and some specific heuristics for different types of events. The final model gives a cumulative F-Score of 52% on the test set, and was the best model on the task. BIBREF35 note that previous approaches on the task suffer due to the pipeline nature and the propagation of errors. To counter this, they adopt a joint inference method based on Markov Logic Networks BIBREF36 for the same task on BioNLP'09. The Markov Logic Network jointly predicts whether each token is a trigger word, and if yes, the class it belongs to; for each dependency edge, whether it is an argument path leading to a “theme” or a “cause”. By formulating the Event Extraction problem using an MLN, the approach becomes computationally feasible and only linear in the length of the sentence. They incorporate hard constraints to encode rules such as “an argument path must have an event”, “a cause path must start with a regulation event”, etc. In addition, they also include some domain specific soft constraints as well as some linguistically-motivated context-specific soft constraints. In order to train the MLN, stochastic gradient descent was used. Certain heuristic methods are implemented in order to deal with errors due to syntactic parsing, especially ambiguities in PP-attachment and coordination. Their final system is competitive and comes very close to the system by BIBREF33 with an average F-Score of 50%. To further improve the system, they suggest leveraging additional joint-inference opportunities and integrating the syntactic parser better. Some other more recent models for Biomedical Event Extraction include BIBREF37 , BIBREF38 . Conclusion We have discussed some of the major problems and challenges in BioIE, and seen some of the diverse approaches adopted to solve them. Some interesting problems such as Pathway Extraction for Biological Systems BIBREF39 , BIBREF40 have not been discussed. Biomedical Information Extraction is a challenging and exciting field for NLP researchers that demands application of state-of-the-art methods. Traditionally, there has been a dependence on hand-crafted features or heavily feature-engineered methods. However, with the advent of deep learning methods, a lot of BioIE tasks are seeing an improvement by adopting deep learning models such as Convolutional Neural Networks and LSTMs, which require minimal feature engineering. Rapid progress in developing better systems for BioIE will be extremely helpful for clinicians and researchers in the Biomedical domain.
What is NER?
cf874cd9023d901e10aa8664b813d32501e7e4d2
What Ġis ĠN ER ?
N amed ĠEntity ĠRecogn ition
<s> What Ġis ĠN ER ? </s> Introduction </s> The Ġexplosion Ġof Ġavailable Ġscientific Ġarticles Ġin Ġthe ĠBi omedical Ġdomain Ġhas Ġled Ġto Ġthe Ġrise Ġof ĠBi omedical ĠInformation ĠExt raction Ġ( Bio IE ). ĠBio IE Ġsystems Ġaim Ġto Ġextract Ġinformation Ġfrom Ġa Ġwide Ġspectrum Ġof Ġarticles Ġincluding Ġmedical Ġliterature , Ġbiological Ġliterature , Ġelectronic Ġhealth Ġrecords , Ġetc . Ġthat Ġcan Ġbe Ġused Ġby Ġclinicians Ġand Ġresearchers Ġin Ġthe Ġfield . ĠOften Ġthe Ġoutputs Ġof ĠBio IE Ġsystems Ġare Ġused Ġto Ġassist Ġin Ġthe Ġcreation Ġof Ġdatabases , Ġor Ġto Ġsuggest Ġnew Ġpaths Ġfor Ġresearch . ĠFor Ġexample , Ġa Ġranked Ġlist Ġof Ġinteracting Ġproteins Ġthat Ġare Ġextracted Ġfrom Ġbiomedical Ġliterature , Ġbut Ġare Ġnot Ġpresent Ġin Ġexisting Ġdatabases , Ġcan Ġallow Ġresearchers Ġto Ġmake Ġinformed Ġdecisions Ġabout Ġwhich Ġprotein / g ene Ġto Ġstudy Ġfurther . ĠInter actions Ġbetween Ġdrugs Ġare Ġnecessary Ġfor Ġclinicians Ġwho Ġsimultaneously Ġadminister Ġmultiple Ġdrugs Ġto Ġtheir Ġpatients . ĠA Ġdatabase Ġof Ġdiseases , Ġtreatments Ġand Ġtests Ġis Ġbeneficial Ġfor Ġdoctors Ġconsulting Ġin Ġcomplicated Ġmedical Ġcases . </s> The Ġmain Ġproblems Ġin ĠBio IE Ġare Ġsimilar Ġto Ġthose Ġin ĠInformation ĠExt raction : </s> This Ġpaper Ġdiscusses , Ġin Ġeach Ġsection , Ġvarious Ġmethods Ġthat Ġhave Ġbeen Ġadopted Ġto Ġsolve Ġthe Ġlisted Ġproblems . ĠEach Ġsection Ġalso Ġhighlights Ġthe Ġdifficulty Ġof ĠInformation ĠExt raction Ġtasks Ġin Ġthe Ġbiomedical Ġdomain . </s> This Ġpaper Ġis Ġintended Ġas Ġa Ġprimer Ġto ĠBi omedical ĠInformation ĠExt raction Ġfor Ġcurrent ĠN LP Ġresearchers . ĠIt Ġaims Ġto Ġhighlight Ġthe Ġdiversity Ġof Ġthe Ġvarious Ġtechniques Ġfrom ĠInformation ĠExt raction Ġthat Ġhave Ġbeen Ġapplied Ġin Ġthe ĠBi omedical Ġdomain . ĠThe Ġstate Ġof Ġbiomedical Ġtext Ġmining Ġis Ġreviewed Ġregularly . ĠFor Ġmore Ġextensive Ġsurveys , Ġconsult ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġ. </s> N amed ĠEntity ĠRecogn ition Ġand ĠFact ĠExt raction </s> N amed ĠEntity ĠRecogn ition Ġ( NER ) Ġin Ġthe ĠBi omedical Ġdomain Ġusually Ġincludes Ġrecognition Ġof Ġentities Ġsuch Ġas Ġproteins , Ġgenes , Ġdiseases , Ġtreatments , Ġdrugs , Ġetc . ĠFact Ġextraction Ġinvolves Ġextraction Ġof ĠNamed ĠEnt ities Ġfrom Ġa Ġcorpus , Ġusually Ġgiven Ġa Ġcertain Ġont ology . ĠWhen Ġcompared Ġto ĠN ER Ġin Ġthe Ġdomain Ġof Ġgeneral Ġtext , Ġthe Ġbiomedical Ġdomain Ġhas Ġsome Ġcharacteristic Ġchallenges : </s> Some Ġof Ġthe Ġearliest Ġsystems Ġwere Ġheavily Ġdependent Ġon Ġhand - crafted Ġfeatures . ĠThe Ġmethod Ġproposed Ġin ĠB IB REF 4 Ġfor Ġrecognition Ġof Ġprotein Ġnames Ġin Ġtext Ġdoes Ġnot Ġrequire Ġany Ġprepared Ġdictionary . ĠThe Ġwork Ġgives Ġexamples Ġof Ġdiversity Ġin Ġprotein Ġnames Ġand Ġlists Ġmultiple Ġrules Ġdepending Ġon Ġsimple Ġword Ġfeatures Ġas Ġwell Ġas ĠPOS Ġtags . </s> BI B REF 5 Ġadopt Ġa Ġmachine Ġlearning Ġapproach Ġfor ĠN ER . ĠTheir ĠN ER Ġsystem Ġextracts Ġmedical Ġproblems , Ġtests Ġand Ġtreatments Ġfrom Ġdischarge Ġsumm aries Ġand Ġprogress Ġnotes . ĠThey Ġuse Ġa Ġsemi - Cond itional ĠRandom ĠField Ġ( se mi - CR F ) ĠB IB REF 6 Ġto Ġoutput Ġlabels Ġover Ġall Ġtokens Ġin Ġthe Ġsentence . ĠThey Ġuse Ġa Ġvariety Ġof Ġtoken , Ġcontext Ġand Ġsentence Ġlevel Ġfeatures . ĠThey Ġalso Ġuse Ġsome Ġconcept Ġmapping Ġfeatures Ġusing Ġexisting Ġannotation Ġtools , Ġas Ġwell Ġas ĠBrown Ġclust ering Ġto Ġform Ġ128 Ġclusters Ġover Ġthe Ġunl ab elled Ġdata . ĠThe Ġdataset Ġused Ġis Ġthe Ġi 2 b 2 Ġ2010 Ġchallenge Ġdataset . ĠTheir Ġsystem Ġachieves Ġan ĠF - Score Ġof Ġ0 . 85 . ĠB IB REF 7 Ġis Ġan Ġincremental Ġpaper Ġon ĠN ER Ġtag gers . ĠIt Ġuses Ġ3 Ġtypes Ġof Ġword - represent ation Ġtechniques Ġ( Brown Ġclust ering , Ġdistribution al Ġclust ering , Ġword Ġvectors ) Ġto Ġimprove Ġperformance Ġof Ġthe ĠN ER ĠCond itional ĠRandom ĠField Ġtag ger , Ġand Ġachieves Ġmarginal ĠF - Score Ġimprovements . </s> BI B REF 8 Ġpropose Ġa Ġboost ra pping Ġmechanism Ġto Ġboot strap Ġbiomedical Ġont ologies Ġusing ĠNE LL ĠB IB REF 9 Ġ, Ġwhich Ġuses Ġa Ġcoupled Ġsemi - super vised Ġboot stra pping Ġapproach Ġto Ġextract Ġfacts Ġfrom Ġtext , Ġgiven Ġan Ġont ology Ġand Ġa Ġsmall Ġnumber Ġof ĠâĢ ľ seed âĢ Ŀ Ġexamples Ġfor Ġeach Ġcategory . ĠThis Ġinteresting Ġapproach Ġ( called ĠBio NE LL ) Ġuses Ġan Ġont ology Ġof Ġover Ġ100 Ġcategories . ĠIn Ġcontrast Ġto ĠNE LL , ĠBio NE LL Ġdoes Ġnot Ġcontain Ġany Ġrelations Ġin Ġthe Ġont ology . ĠBio NE LL Ġis Ġmotivated Ġby Ġthe Ġfact Ġthat Ġa Ġlot Ġof Ġscientific Ġliterature Ġavailable Ġonline Ġis Ġhighly Ġreliable Ġdue Ġto Ġpeer - review . ĠThe Ġauthors Ġnote Ġthat Ġthe Ġalgorithm Ġused Ġby ĠNE LL Ġto Ġboot strap Ġfails Ġin ĠBio NE LL Ġdue Ġto Ġambig u ities Ġin Ġbiomedical Ġliterature , Ġand Ġheavy Ġsemantic Ġdrift . ĠOne Ġof Ġthe Ġcauses Ġfor Ġthis Ġis Ġthat Ġoften Ġcommon Ġwords Ġsuch Ġas ĠâĢ ľ white âĢ Ŀ , ĠâĢ ľ dad âĢ Ŀ , ĠâĢ ľ arm âĢ Ŀ Ġare Ġused Ġas Ġnames Ġof Ġgenes - Ġthis Ġcan Ġeasily Ġresult Ġin Ġsemantic Ġdrift Ġin Ġone Ġiteration Ġof Ġthe Ġboot stra pping . ĠIn Ġorder Ġto Ġmitigate Ġthis , Ġthey Ġuse ĠPoint wise ĠMutual ĠInformation Ġscores Ġfor Ġcorpus Ġlevel Ġstatistics , Ġwhich Ġattributes Ġa Ġsmall Ġscore Ġto Ġcommon Ġwords . ĠIn Ġaddition , Ġin Ġcontrast Ġto ĠNE LL , ĠBio NE LL Ġonly Ġuses Ġhigh Ġinstances Ġas Ġseeds Ġin Ġthe Ġnext Ġiteration , Ġbut Ġadds Ġlow Ġranking Ġinstances Ġto Ġthe Ġknowledge Ġbase . ĠSince Ġevaluation Ġis Ġnot Ġpossible Ġusing ĠMechanical ĠTurk Ġor Ġa Ġsmall Ġnumber Ġof Ġexperts Ġ( due Ġto Ġthe Ġcomplexity Ġof Ġthe Ġtask ), Ġthey Ġuse ĠFree base ĠB IB REF 10 Ġ, Ġa Ġknowledge Ġbase Ġthat Ġhas Ġsome Ġbiomedical Ġconcepts Ġas Ġwell . ĠThe Ġlex icon Ġlearned Ġusing ĠBio NE LL Ġis Ġused Ġto Ġtrain Ġan ĠN ER Ġsystem . ĠThe Ġsystem Ġshows Ġa Ġvery Ġhigh Ġprecision , Ġthereby Ġshowing Ġthat ĠBio NE LL Ġlearns Ġvery Ġfew Ġambiguous Ġterms . </s> More Ġrecently , Ġdeep Ġlearning Ġtechniques Ġhave Ġbeen Ġdeveloped Ġto Ġfurther Ġenhance Ġthe Ġperformance Ġof ĠN ER Ġsystems . ĠB IB REF 11 Ġexplore Ġrecurrent Ġneural Ġnetworks Ġfor Ġthe Ġproblem Ġof ĠN ER Ġin Ġbiomedical Ġtext . </s> Rel ation ĠExt raction </s> In ĠBi omedical ĠInformation ĠExt raction , ĠRel ation ĠExt raction Ġinvolves Ġfinding Ġrelated Ġentities Ġof Ġmany Ġdifferent Ġkinds . ĠSome Ġof Ġthese Ġinclude Ġprotein - protein Ġinteractions , Ġdisease - g ene Ġrelations Ġand Ġdrug - drug Ġinteractions . ĠDue Ġto Ġthe Ġexplosion Ġof Ġavailable Ġbiomedical Ġliterature , Ġit Ġis Ġimpossible Ġfor Ġone Ġperson Ġto Ġextract Ġrelevant Ġrelations Ġfrom Ġpublished Ġmaterial . ĠAutomatic Ġextraction Ġof Ġrelations Ġassists Ġin Ġthe Ġprocess Ġof Ġdatabase Ġcreation , Ġby Ġsuggesting Ġpotentially Ġrelated Ġentities Ġwith Ġlinks Ġto Ġthe Ġsource Ġarticle . ĠFor Ġexample , Ġa Ġdatabase Ġof Ġdrug - drug Ġinteractions Ġis Ġimportant Ġfor Ġclinicians Ġwho Ġadminister Ġmultiple Ġdrugs Ġsimultaneously Ġto Ġtheir Ġpatients - Ġit Ġis Ġimperative Ġto Ġknow Ġif Ġone Ġdrug Ġwill Ġhave Ġan Ġadverse Ġeffect Ġon Ġthe Ġother . ĠA Ġvariety Ġof Ġmethods Ġhave Ġbeen Ġdeveloped Ġfor Ġrelation Ġextract ions , Ġand Ġare Ġoften Ġinspired Ġby ĠRel ation ĠExt raction Ġin ĠN LP Ġtasks . ĠThese Ġinclude Ġrule - based Ġapproaches , Ġhand - crafted Ġpatterns , Ġfeature - based Ġand Ġkernel Ġmachine Ġlearning Ġmethods , Ġand Ġmore Ġrecently Ġdeep Ġlearning Ġarchitectures . ĠRel ation ĠExt raction Ġsystems Ġover ĠBi omedical ĠCorpor a Ġare Ġoften Ġaffected Ġby Ġnoisy Ġextraction Ġof Ġentities , Ġdue Ġto Ġambig u ities Ġin Ġnames Ġof Ġproteins , Ġgenes , Ġdrugs Ġetc . </s> BI B REF 12 Ġwas Ġone Ġof Ġthe Ġfirst Ġlarge Ġscale ĠInformation ĠExt raction Ġefforts Ġto Ġstudy Ġthe Ġfeasibility Ġof Ġextraction Ġof Ġprotein - protein Ġinteractions Ġ( such Ġas ĠâĢ ľ protein ĠA Ġactivates Ġprotein ĠB ") Ġfrom ĠBi omedical Ġtext . ĠUsing Ġ8 Ġhand - crafted Ġregular Ġexpressions Ġover Ġa Ġfixed Ġvocabulary , Ġthe Ġauthors Ġwere Ġable Ġto Ġachieve Ġa Ġrecall Ġof Ġ30 % Ġfor Ġinteractions Ġpresent Ġin ĠThe ĠDictionary Ġof ĠInter acting ĠPro te ins Ġ( D IP ) Ġfrom Ġabstract s Ġin ĠMedline . ĠThe Ġmethod Ġdid Ġnot Ġdifferentiate Ġbetween Ġthe Ġtype Ġof Ġrelation . ĠThe Ġreasons Ġfor Ġthe Ġlow Ġrecall Ġwere Ġthe Ġinconsistency Ġin Ġprotein Ġn omen cl ature , Ġinformation Ġnot Ġpresent Ġin Ġthe Ġabstract , Ġand Ġdue Ġto Ġspecificity Ġof Ġthe Ġhand - crafted Ġpatterns . ĠOn Ġa Ġsmall Ġsubset Ġof Ġextracted Ġrelations , Ġthey Ġfound Ġthat Ġabout Ġ60 % Ġwere Ġtrue Ġinteractions Ġbetween Ġproteins Ġnot Ġpresent Ġin ĠD IP . </s> BI B REF 13 Ġcombine Ġsentence Ġlevel Ġrelation Ġextraction Ġfor Ġprotein Ġinteractions Ġwith Ġcorpus Ġlevel Ġstatistics . ĠSimilar Ġto ĠB IB REF 12 Ġ, Ġthey Ġdo Ġnot Ġconsider Ġthe Ġtype Ġof Ġinteraction Ġbetween Ġproteins - Ġonly Ġwhether Ġthey Ġinteract Ġin Ġthe Ġgeneral Ġsense Ġof Ġthe Ġword . ĠThey Ġalso Ġdo Ġnot Ġdifferentiate Ġbetween Ġgenes Ġand Ġtheir Ġprotein Ġproducts Ġ( which Ġmay Ġshare Ġthe Ġsame Ġname ). ĠThey Ġuse ĠPoint wise ĠMutual ĠInformation Ġ( PM I ) Ġfor Ġcorpus Ġlevel Ġstatistics Ġto Ġdetermine Ġwhether Ġa Ġpair Ġof Ġproteins Ġoccur Ġtogether Ġby Ġchance Ġor Ġbecause Ġthey Ġinteract . ĠThey Ġcombine Ġthis Ġwith Ġa Ġconfidence Ġaggreg ator Ġthat Ġtakes Ġthe Ġmaximum Ġof Ġthe Ġconfidence Ġof Ġthe Ġextract or Ġover Ġall Ġextract ions Ġfor Ġthe Ġsame Ġprotein - pair . ĠThe Ġextraction Ġuses Ġa Ġsubsequ ence Ġkernel Ġbased Ġon ĠB IB REF 14 Ġ. ĠThe Ġintegrated Ġmodel , Ġthat Ġcombines ĠPM I Ġwith Ġaggregate Ġconfidence , Ġgives Ġthe Ġbest Ġperformance . ĠKernel Ġmethods Ġhave Ġwidely Ġbeen Ġstudied Ġfor ĠRel ation ĠExt raction Ġin ĠBi omedical ĠLiterature . ĠCommon Ġkernels Ġused Ġusually Ġexploit Ġlinguistic Ġinformation Ġby Ġutil ising Ġkernels Ġbased Ġon Ġthe Ġdependency Ġtree ĠB IB REF 15 Ġ, ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ. </s> BI B REF 18 Ġlook Ġat Ġthe Ġextraction Ġof Ġdiseases Ġand Ġtheir Ġrelevant Ġgenes . ĠThey Ġuse Ġa Ġdictionary Ġfrom Ġsix Ġpublic Ġdatabases Ġto Ġannot ate Ġgenes Ġand Ġdiseases Ġin ĠMedline Ġabstract s . ĠIn Ġtheir Ġwork , Ġthe Ġauthors Ġnote Ġthat Ġwhen Ġboth Ġgenes Ġand Ġdiseases Ġare Ġcorrectly Ġidentified , Ġthey Ġare Ġrelated Ġin Ġ94 % Ġof Ġthe Ġcases . ĠThe Ġproblem Ġthen Ġreduces Ġto Ġfiltering Ġincorrect Ġmatches Ġusing Ġthe Ġdictionary , Ġwhich Ġoccurs Ġdue Ġto Ġfalse Ġpositives Ġresulting Ġfrom Ġambig u ities Ġin Ġthe Ġnames Ġas Ġwell Ġas Ġambig u ities Ġin Ġabbrevi ations . ĠTo Ġthis Ġend , Ġthey Ġtrain Ġa ĠMax - Ent Ġbased ĠN ER Ġclass ifier Ġfor Ġthe Ġtask , Ġand Ġget Ġa Ġ26 % Ġgain Ġin Ġprecision Ġover Ġthe Ġunf il tered Ġbaseline , Ġwith Ġa Ġslight Ġhit Ġin Ġrecall . ĠThey Ġuse ĠPOS Ġtags , Ġexpanded Ġforms Ġof Ġabbrevi ations , Ġindicators Ġfor ĠGreek Ġletters Ġas Ġwell Ġas Ġsuffix es Ġand Ġprefix es Ġcommonly Ġused Ġin Ġbiomedical Ġterms . </s> BI B REF 19 Ġadopt Ġa Ġsupervised Ġfeature - based Ġapproach Ġfor Ġthe Ġextraction Ġof Ġdrug - drug Ġinteraction Ġ( DD I ) Ġfor Ġthe ĠD DI - 2013 Ġdataset ĠB IB REF 20 Ġ. ĠThey Ġpartition Ġthe Ġdata Ġin Ġsubs ets Ġdepending Ġon Ġthe Ġsynt actic Ġfeatures , Ġand Ġtrain Ġa Ġdifferent Ġmodel Ġfor Ġeach . ĠThey Ġuse Ġlex ical , Ġsynt actic Ġand Ġverb Ġbased Ġfeatures Ġon Ġtop Ġof Ġshallow Ġparse Ġfeatures , Ġin Ġaddition Ġto Ġa Ġhand - crafted Ġlist Ġof Ġtrigger Ġwords Ġto Ġdefine Ġtheir Ġfeatures . ĠAn ĠS VM Ġclass ifier Ġis Ġthen Ġtrained Ġon Ġthe Ġfeature Ġvectors , Ġwith Ġa Ġpositive Ġlabel Ġif Ġthe Ġdrug Ġpair Ġinteracts , Ġand Ġnegative Ġotherwise . ĠTheir Ġmethod Ġbeats Ġother Ġsystems Ġon Ġthe ĠD DI - 2013 Ġdataset . ĠSome Ġother Ġfeature - based Ġapproaches Ġare Ġdescribed Ġin ĠB IB REF 21 Ġ, ĠB IB REF 22 Ġ. </s> D istant Ġsupervision Ġmethods Ġhave Ġalso Ġbeen Ġapplied Ġto Ġrelation Ġextraction Ġover Ġbiomedical Ġcorpor a . ĠIn ĠB IB REF 23 Ġ, Ġ10 , 000 Ġneuroscience Ġarticles Ġare Ġdist antly Ġsupervised Ġusing Ġinformation Ġfrom ĠU ML S ĠSem antic ĠNetwork Ġto Ġclassify Ġbrain - g ene Ġrelations Ġinto Ġgene Exp ression Ġand Ġother Rel ation . ĠThey Ġuse Ġlex ical Ġ( bag Ġof Ġwords , Ġcontextual ) Ġfeatures Ġas Ġwell Ġas Ġsynt actic Ġ( depend ency Ġparse Ġfeatures ). ĠThey Ġmake Ġthe ĠâĢ ľ at - le ast Ġone âĢ Ŀ Ġassumption , Ġi . e . Ġat Ġleast Ġone Ġof Ġthe Ġsentences Ġextracted Ġfor Ġa Ġgiven Ġentity - pair Ġcontains Ġthe Ġrelation Ġin Ġdatabase . ĠThey Ġmodel Ġit Ġas Ġa Ġmulti - instance Ġlearning Ġproblem Ġand Ġadopt Ġa Ġgraphical Ġmodel Ġsimilar Ġto ĠB IB REF 24 Ġ. ĠThey Ġtest Ġusing Ġmanually Ġannot ated Ġexamples . ĠThey Ġnote Ġthat Ġthe ĠF - score Ġachieved Ġare Ġmuch Ġlesser Ġthan Ġthat Ġachieved Ġin Ġthe Ġgeneral Ġdomain Ġin ĠB IB REF 24 Ġ, Ġand Ġattribute Ġto Ġgenerally Ġpoorer Ġperformance Ġof ĠN ER Ġtools Ġin Ġthe Ġbiomedical Ġdomain , Ġas Ġwell Ġas Ġless Ġtraining Ġexamples . ĠB IB REF 25 Ġexplore Ġdistant Ġsupervision Ġmethods Ġfor Ġprotein - protein Ġinteraction Ġextraction . </s> More Ġrecently , Ġdeep Ġlearning Ġmethods Ġhave Ġbeen Ġapplied Ġto Ġrelation Ġextraction Ġin Ġthe Ġbiomedical Ġdomain . ĠOne Ġof Ġthe Ġmain Ġadvantages Ġof Ġsuch Ġmethods Ġover Ġtraditional Ġfeature Ġor Ġkernel Ġbased Ġlearning Ġmethods Ġis Ġthat Ġthey Ġrequire Ġminimal Ġfeature Ġengineering . ĠIn ĠB IB REF 26 Ġ, Ġskip - gram Ġvectors ĠB IB REF 27 Ġare Ġtrained Ġover Ġ5 . 6 Gb Ġof Ġunl ab elled Ġtext . ĠThey Ġuse Ġthese Ġvectors Ġto Ġextract Ġprotein - protein Ġinteractions Ġby Ġconverting Ġthem Ġinto Ġfeatures Ġfor Ġentities , Ġcontext Ġand Ġthe Ġentire Ġsentence . ĠUsing Ġan ĠS VM Ġfor Ġclassification , Ġtheir Ġmethod Ġis Ġable Ġto Ġoutper form Ġmany Ġkernel Ġand Ġfeature Ġbased Ġmethods Ġover Ġa Ġvariety Ġof Ġdatasets . </s> BI B REF 28 Ġfollow Ġa Ġsimilar Ġmethod Ġby Ġusing Ġword Ġvectors Ġtrained Ġon ĠPubMed Ġarticles . ĠThey Ġuse Ġit Ġfor Ġthe Ġtask Ġof Ġrelation Ġextraction Ġfrom Ġclinical Ġtext Ġfor Ġentities Ġthat Ġinclude Ġproblem , Ġtreatment Ġand Ġmedical Ġtest . ĠFor Ġa Ġgiven Ġsentence , Ġgiven Ġlabelled Ġentities , Ġthey Ġpredict Ġthe Ġtype Ġof Ġrelation Ġexhibited Ġ( or ĠNone ) Ġby Ġthe Ġentity Ġpair . ĠThese Ġtypes Ġinclude ĠâĢ ľ treatment Ġcaused Ġmedical Ġproblem âĢ Ŀ , ĠâĢ ľ test Ġconducted Ġto Ġinvestigate Ġmedical Ġproblem âĢ Ŀ , ĠâĢ ľ medical Ġproblem Ġindicates Ġmedical Ġproblems âĢ Ŀ , Ġetc . ĠThey Ġuse Ġa ĠConv olution al ĠNeural ĠNetwork Ġ( CNN ) Ġfollowed Ġby Ġfeed forward Ġneural Ġnetwork Ġarchitecture Ġfor Ġprediction . ĠIn Ġaddition Ġto Ġpre - trained Ġword Ġvectors Ġas Ġfeatures , Ġfor Ġeach Ġtoken Ġthey Ġalso Ġadd Ġfeatures Ġfor ĠPOS Ġtags , Ġdistance Ġfrom Ġboth Ġthe Ġentities Ġin Ġthe Ġsentence , Ġas Ġwell ĠB IO Ġtags Ġfor Ġthe Ġentities . ĠTheir Ġmodel Ġperforms Ġbetter Ġthan Ġa Ġfeature Ġbased ĠS VM Ġbaseline Ġthat Ġthey Ġtrain Ġthemselves . </s> The ĠBio N LP ' 16 ĠShared ĠT asks Ġhas Ġalso Ġintroduced Ġsome ĠRel ation ĠExt raction Ġtasks , Ġin Ġparticular Ġthe ĠBB 3 - event Ġsubt ask Ġthat Ġinvolves Ġpredicting Ġwhether Ġa ĠâĢ ľ l ives - in âĢ Ŀ Ġrelation Ġholds Ġfor Ġa ĠB acteria Ġin Ġa Ġlocation . ĠSome Ġof Ġthe Ġtop Ġperforming Ġmodels Ġfor Ġthis Ġtask Ġare Ġdeep Ġlearning Ġmodels . ĠB IB REF 29 Ġtrain Ġword Ġembed d ings Ġwith Ġsix Ġbillions Ġwords Ġof Ġscientific Ġtexts Ġfrom ĠPubMed . ĠThey Ġthen Ġconsider Ġthe Ġshortest Ġdependency Ġpath Ġbetween Ġthe Ġtwo Ġentities Ġ( B acteria Ġand Ġlocation ). ĠFor Ġeach Ġtoken Ġin Ġthe Ġpath , Ġthey Ġuse Ġword Ġembed ding Ġfeatures , ĠPOS Ġtype Ġembed d ings Ġand Ġdependency Ġtype Ġembed d ings . ĠThey Ġtrain Ġa Ġun id irection al ĠL ST M ĠB IB REF 30 Ġover Ġthe Ġdependency Ġpath , Ġthat Ġachieves Ġan ĠF - Score Ġof Ġ52 . 1 % Ġon Ġthe Ġtest Ġset . </s> BI B REF 31 Ġimprove Ġthe Ġperformance Ġby Ġmaking Ġmodifications Ġto Ġthe Ġabove Ġmodel . ĠInstead Ġof Ġusing Ġthe Ġshortest Ġdependency Ġpath , Ġthey Ġmodify Ġthe Ġparse Ġtree Ġbased Ġon Ġsome Ġpr uning Ġstrategies . ĠThey Ġalso Ġadd Ġfeature Ġembed d ings Ġfor Ġeach Ġtoken Ġto Ġrepresent Ġthe Ġdistance Ġfrom Ġthe Ġentities Ġin Ġthe Ġshortest Ġpath . ĠThey Ġthen Ġtrain Ġa ĠBid irection al ĠL ST M Ġon Ġthe Ġpath , Ġand Ġobtain Ġan ĠF - Score Ġof Ġ57 . 1 %. </s> The Ġrecent Ġsuccess Ġof Ġdeep Ġlearning Ġmodels Ġin ĠBi omedical ĠRel ation ĠExt raction Ġthat Ġrequire Ġminimal Ġfeature Ġengineering Ġis Ġpromising . ĠThis Ġalso Ġsuggests Ġnew Ġavenues Ġof Ġresearch Ġin Ġthe Ġfield . ĠAn Ġapproach Ġas Ġin ĠB IB REF 32 Ġcan Ġbe Ġused Ġto Ġcombine Ġmulti - instance Ġlearning Ġand Ġdistant Ġsupervision Ġwith Ġa Ġneural Ġarchitecture . </s> Event ĠExt raction </s> Event ĠExt raction Ġin Ġthe ĠBi omedical Ġdomain Ġis Ġa Ġtask Ġthat Ġhas Ġgained Ġmore Ġimportance Ġrecently . ĠEvent ĠExt raction Ġgoes Ġbeyond ĠRel ation ĠExt raction . ĠIn ĠBi omedical ĠEvent ĠExt raction , Ġevents Ġgenerally Ġrefer Ġto Ġa Ġchange Ġin Ġthe Ġstate Ġof Ġbiological Ġmolecules Ġsuch Ġas Ġproteins Ġand ĠDNA . ĠGenerally , Ġit Ġincludes Ġdetection Ġof Ġtargeted Ġevent Ġtypes Ġsuch Ġas Ġgene Ġexpression , Ġregulation , Ġlocal isation Ġand Ġtranscription . ĠEach Ġevent Ġtype Ġin Ġaddition Ġcan Ġhave Ġmultiple Ġarguments Ġthat Ġneed Ġto Ġbe Ġdetected . ĠAn Ġadditional Ġlayer Ġof Ġcomplexity Ġcomes Ġfrom Ġthe Ġfact Ġthat Ġevents Ġcan Ġalso Ġbe Ġarguments Ġof Ġother Ġevents , Ġgiving Ġrise Ġto Ġa Ġnested Ġstructure . ĠThis Ġhelps Ġto Ġcapture Ġthe Ġunderlying Ġbiology Ġbetter ĠB IB REF 1 Ġ. ĠDetect ing Ġthe Ġevent Ġtype Ġoften Ġinvolves Ġrecogn ising Ġand Ġclass ifying Ġtrigger Ġwords . ĠOften , Ġthese Ġwords Ġare Ġverbs Ġsuch Ġas ĠâĢ ľ activ ates âĢ Ŀ , ĠâĢ ľ in hib its âĢ Ŀ , ĠâĢ ľ ph osph ory lation âĢ Ŀ Ġthat Ġmay Ġindicate Ġa Ġsingle , Ġor Ġsometimes Ġmultiple Ġevent Ġtypes . ĠIn Ġthis Ġsection , Ġwe Ġwill Ġdiscuss Ġsome Ġof Ġthe Ġsuccessful Ġmodels Ġfor ĠEvent ĠExt raction Ġin Ġsome Ġdetail . </s> Event ĠExt raction Ġgained Ġa Ġlot Ġof Ġinterest Ġwith Ġthe Ġavailability Ġof Ġan Ġannot ated Ġcorpus Ġwith Ġthe ĠBio N LP ' 09 ĠShared ĠTask Ġon ĠEvent ĠExt raction ĠB IB REF 34 Ġ. ĠThe Ġtask Ġinvolves Ġprediction Ġof Ġtrigger Ġwords Ġover Ġnine Ġevent Ġtypes Ġsuch Ġas Ġexpression , Ġtranscription , Ġcat abol ism , Ġbinding , Ġetc . Ġgiven Ġonly Ġannotation Ġof Ġnamed Ġentities Ġ( pro te ins , Ġgenes , Ġetc .). ĠFor Ġeach Ġevent , Ġits Ġclass , Ġtrigger Ġexpression Ġand Ġarguments Ġneed Ġto Ġbe Ġextracted . ĠSince Ġthe Ġevents Ġcan Ġbe Ġarguments Ġto Ġother Ġevents , Ġthe Ġfinal Ġoutput Ġin Ġgeneral Ġis Ġa Ġgraph Ġrepresentation Ġwith Ġevents Ġand Ġnamed Ġentities Ġas Ġnodes , Ġand Ġedges Ġthat Ġcorrespond Ġto Ġevent Ġarguments . ĠB IB REF 33 Ġpresent Ġa Ġpipeline Ġbased Ġmethod Ġthat Ġis Ġheavily Ġdependent Ġon Ġdependency Ġparsing . ĠTheir Ġpipeline Ġapproach Ġconsists Ġof Ġthree Ġsteps : Ġtrigger Ġdetection , Ġargument Ġdetection Ġand Ġsemantic Ġpost - processing . ĠWhile Ġthe Ġfirst Ġtwo Ġcomponents Ġare Ġlearning Ġbased Ġsystems , Ġthe Ġlast Ġcomponent Ġis Ġa Ġrule Ġbased Ġsystem . ĠFor Ġthe ĠBio N LP ' 09 Ġcorpus , Ġonly Ġ5 % Ġof Ġthe Ġevents Ġspan Ġmultiple Ġsentences . ĠHence Ġthe Ġapproach Ġdoes Ġnot Ġget Ġaffected Ġseverely Ġby Ġconsidering Ġonly Ġsingle Ġsentences . ĠIt Ġis Ġimportant Ġto Ġnote Ġthat Ġtrigger Ġwords Ġcannot Ġsimply Ġbe Ġreduced Ġto Ġa Ġdictionary Ġlookup . ĠThis Ġis Ġbecause Ġa Ġspecific Ġword Ġmay Ġbelong Ġto Ġmultiple Ġclasses , Ġor Ġmay Ġnot Ġalways Ġbe Ġa Ġtrigger Ġword Ġfor Ġan Ġevent . ĠFor Ġexample , ĠâĢ ľ activate âĢ Ŀ Ġis Ġfound Ġto Ġnot Ġbe Ġa Ġtrigger Ġword Ġin Ġover Ġ70 % Ġof Ġthe Ġcases . ĠA Ġmulti - class ĠS VM Ġis Ġtrained Ġfor Ġtrigger Ġdetection Ġon Ġeach Ġtoken , Ġusing Ġa Ġlarge Ġfeature Ġset Ġconsisting Ġof Ġsemantic Ġand Ġsynt actic Ġfeatures . ĠIt Ġis Ġinteresting Ġto Ġnote Ġthat Ġthe Ġhyper param eters Ġof Ġthis Ġclass ifier Ġare Ġoptim ised Ġbased Ġon Ġthe Ġperformance Ġof Ġthe Ġentire Ġend - to - end Ġsystem . </s> For Ġthe Ġsecond Ġcomponent Ġto Ġdetect Ġarguments , Ġlabels Ġfor Ġedges Ġbetween Ġentities Ġmust Ġbe Ġpredicted . ĠFor Ġthe ĠBio N LP ' 09 ĠShared ĠTask , Ġeach Ġdirected Ġedge Ġfrom Ġone Ġevent Ġnode Ġto Ġanother Ġevent Ġnode , Ġor Ġfrom Ġan Ġevent Ġnode Ġto Ġa Ġnamed Ġentity Ġnode Ġare Ġclassified Ġas ĠâĢ ľ theme âĢ Ŀ , ĠâĢ ľ cause âĢ Ŀ , Ġor ĠNone . ĠThe Ġsecond Ġcomponent Ġof Ġthe Ġpipeline Ġmakes Ġthese Ġpredictions Ġindependently . ĠThis Ġis Ġalso Ġtrained Ġusing Ġa Ġmulti - class ĠS VM Ġwhich Ġinvolves Ġheavy Ġuse Ġof Ġsynt actic Ġfeatures , Ġincluding Ġthe Ġshortest Ġdependency Ġpath Ġbetween Ġthe Ġnodes . ĠThe Ġauthors Ġnote Ġthat Ġthe Ġprecision - rec all Ġchoice Ġof Ġthe Ġfirst Ġcomponent Ġaffects Ġthe Ġperformance Ġof Ġthe Ġsecond Ġcomponent : Ġsince Ġthe Ġsecond Ġcomponent Ġis Ġonly Ġtrained Ġon ĠGold Ġexamples , Ġany Ġerror Ġby Ġthe Ġfirst Ġcomponent Ġwill Ġlead Ġto Ġa Ġcasc ading Ġof Ġerrors . ĠThe Ġfinal Ġcomponent , Ġwhich Ġis Ġa Ġsemantic Ġpost - processing Ġstep , Ġconsists Ġof Ġrules Ġand Ġhe ur istics Ġto Ġcorrect Ġthe Ġoutput Ġof Ġthe Ġsecond Ġcomponent . ĠSince Ġthe Ġedge Ġpredictions Ġare Ġmade Ġindependently , Ġit Ġis Ġpossible Ġthat Ġsome Ġevent Ġnodes Ġdo Ġnot Ġhave Ġany Ġedges , Ġor Ġhave Ġan Ġimproper Ġcombination Ġof Ġedges . ĠThe Ġrule Ġbased Ġcomponent Ġcorrect s Ġthese Ġand Ġapplies Ġrules Ġto Ġbreak Ġdirected Ġcycles Ġin Ġthe Ġgraph , Ġand Ġsome Ġspecific Ġhe ur istics Ġfor Ġdifferent Ġtypes Ġof Ġevents . ĠThe Ġfinal Ġmodel Ġgives Ġa Ġcumulative ĠF - Score Ġof Ġ52 % Ġon Ġthe Ġtest Ġset , Ġand Ġwas Ġthe Ġbest Ġmodel Ġon Ġthe Ġtask . </s> BI B REF 35 Ġnote Ġthat Ġprevious Ġapproaches Ġon Ġthe Ġtask Ġsuffer Ġdue Ġto Ġthe Ġpipeline Ġnature Ġand Ġthe Ġpropagation Ġof Ġerrors . ĠTo Ġcounter Ġthis , Ġthey Ġadopt Ġa Ġjoint Ġinference Ġmethod Ġbased Ġon ĠMark ov ĠLogic ĠNetworks ĠB IB REF 36 Ġfor Ġthe Ġsame Ġtask Ġon ĠBio N LP ' 09 . ĠThe ĠMark ov ĠLogic ĠNetwork Ġjointly Ġpredicts Ġwhether Ġeach Ġtoken Ġis Ġa Ġtrigger Ġword , Ġand Ġif Ġyes , Ġthe Ġclass Ġit Ġbelongs Ġto ; Ġfor Ġeach Ġdependency Ġedge , Ġwhether Ġit Ġis Ġan Ġargument Ġpath Ġleading Ġto Ġa ĠâĢ ľ theme âĢ Ŀ Ġor Ġa ĠâĢ ľ cause âĢ Ŀ . ĠBy Ġform ulating Ġthe ĠEvent ĠExt raction Ġproblem Ġusing Ġan ĠML N , Ġthe Ġapproach Ġbecomes Ġcomput ationally Ġfeasible Ġand Ġonly Ġlinear Ġin Ġthe Ġlength Ġof Ġthe Ġsentence . ĠThey Ġincorporate Ġhard Ġconstraints Ġto Ġencode Ġrules Ġsuch Ġas ĠâĢ ľ an Ġargument Ġpath Ġmust Ġhave Ġan Ġevent âĢ Ŀ , ĠâĢ ľ a Ġcause Ġpath Ġmust Ġstart Ġwith Ġa Ġregulation Ġevent âĢ Ŀ , Ġetc . ĠIn Ġaddition , Ġthey Ġalso Ġinclude Ġsome Ġdomain Ġspecific Ġsoft Ġconstraints Ġas Ġwell Ġas Ġsome Ġlingu istically - mot ivated Ġcontext - specific Ġsoft Ġconstraints . ĠIn Ġorder Ġto Ġtrain Ġthe ĠML N , Ġsto ch astic Ġgradient Ġdescent Ġwas Ġused . ĠCertain Ġhe uristic Ġmethods Ġare Ġimplemented Ġin Ġorder Ġto Ġdeal Ġwith Ġerrors Ġdue Ġto Ġsynt actic Ġparsing , Ġespecially Ġambig u ities Ġin ĠPP - att achment Ġand Ġcoordination . ĠTheir Ġfinal Ġsystem Ġis Ġcompetitive Ġand Ġcomes Ġvery Ġclose Ġto Ġthe Ġsystem Ġby ĠB IB REF 33 Ġwith Ġan Ġaverage ĠF - Score Ġof Ġ50 %. ĠTo Ġfurther Ġimprove Ġthe Ġsystem , Ġthey Ġsuggest Ġleveraging Ġadditional Ġjoint - in ference Ġopportunities Ġand Ġintegrating Ġthe Ġsynt actic Ġparser Ġbetter . ĠSome Ġother Ġmore Ġrecent Ġmodels Ġfor ĠBi omedical ĠEvent ĠExt raction Ġinclude ĠB IB REF 37 Ġ, ĠB IB REF 38 Ġ. </s> Conclusion </s> We Ġhave Ġdiscussed Ġsome Ġof Ġthe Ġmajor Ġproblems Ġand Ġchallenges Ġin ĠBio IE , Ġand Ġseen Ġsome Ġof Ġthe Ġdiverse Ġapproaches Ġadopted Ġto Ġsolve Ġthem . ĠSome Ġinteresting Ġproblems Ġsuch Ġas ĠPath way ĠExt raction Ġfor ĠBiological ĠSystems ĠB IB REF 39 Ġ, ĠB IB REF 40 Ġhave Ġnot Ġbeen Ġdiscussed . </s> Bi omedical ĠInformation ĠExt raction Ġis Ġa Ġchallenging Ġand Ġexciting Ġfield Ġfor ĠN LP Ġresearchers Ġthat Ġdemands Ġapplication Ġof Ġstate - of - the - art Ġmethods . ĠTrad itionally , Ġthere Ġhas Ġbeen Ġa Ġdependence Ġon Ġhand - crafted Ġfeatures Ġor Ġheavily Ġfeature - engine ered Ġmethods . ĠHowever , Ġwith Ġthe Ġadvent Ġof Ġdeep Ġlearning Ġmethods , Ġa Ġlot Ġof ĠBio IE Ġtasks Ġare Ġseeing Ġan Ġimprovement Ġby Ġadopting Ġdeep Ġlearning Ġmodels Ġsuch Ġas ĠConv olution al ĠNeural ĠNetworks Ġand ĠL ST Ms , Ġwhich Ġrequire Ġminimal Ġfeature Ġengineering . ĠRapid Ġprogress Ġin Ġdeveloping Ġbetter Ġsystems Ġfor ĠBio IE Ġwill Ġbe Ġextremely Ġhelpful Ġfor Ġclinicians Ġand Ġresearchers Ġin Ġthe ĠBi omedical Ġdomain .