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Part-of-speech tagging happens after language detection. It labels each word with a tag that indicates its syntactic role in the sentence. For instance, a word could be a noun, verb, adjective or adverb (or other syntactic tag). We used Freeling BIBREF8 library to provide the tags. This library resorts to a Hidden Markov Model as described by Brants BIBREF9. The end result is a tag for each word as described by the EAGLES tagset . We use the named entity recognition module after part-of-speech tagging. This module labels each part of the sentence into different categories such as "PERSON", "LOCATION", or "ORGANIZATION". We also used Freeling to label the named entities and the details of the algorithm are shown in the paper by Carreras et al BIBREF10. Aside from the three aforementioned categories, we also extracted "DATE/TIME" and "CURRENCY" values by looking at the part-of-speech tags: date/time words have a tag of "W", while currencies have "Zm". Dependency parsing involves tagging a word based on different features to indicate if it is dependent on another word. The Freeling library also has dependency parsing models for Portuguese. Since we wanted to build a SRL (Semantic Role Labeling) module on top of the dependency parser and the current released version of Freeling does not have an SRL module for Portuguese, we trained a different Portuguese dependency parsing model that was compatible (in terms of used tags) with the available annotated.
1909.05359
Introduction </s> The Ġautomatic Ġidentification , Ġextraction Ġand Ġrepresentation Ġof Ġthe Ġinformation Ġconveyed Ġin Ġtexts Ġis Ġa Ġkey Ġtask Ġnowadays . ĠIn Ġfact , Ġthis Ġresearch Ġtopic Ġis Ġincreasing Ġits Ġrelevance Ġwith Ġthe Ġexponential Ġgrowth Ġof Ġsocial Ġnetworks Ġand Ġthe Ġneed Ġto Ġhave Ġtools Ġthat Ġare Ġable Ġto Ġautomatically Ġprocess Ġthem ĠB IB REF 0 . </s> Some Ġof Ġthe Ġdomains Ġwhere Ġit Ġis Ġmore Ġimportant Ġto Ġbe Ġable Ġto Ġperform Ġthis Ġkind Ġof Ġaction Ġare Ġthe Ġjur id ical Ġand Ġlegal Ġones . ĠEffect ively , Ġit Ġis Ġcrucial Ġto Ġhave Ġthe Ġcapability Ġto Ġanalyse Ġopen Ġaccess Ġtext Ġsources , Ġlike Ġsocial Ġnets Ġ( Twitter Ġand ĠFacebook , Ġfor Ġinstance ), Ġblogs , Ġonline Ġnewspapers , Ġand Ġto Ġbe Ġable Ġto Ġextract Ġthe Ġrelevant Ġinformation Ġand Ġrepresent Ġit Ġin Ġa Ġknowledge Ġbase , Ġallowing Ġposterior Ġinf erences Ġand Ġreasoning . </s> In Ġthe Ġcontext Ġof Ġthis Ġwork , Ġwe Ġwill Ġpresent Ġresults Ġof Ġthe ĠR & D Ġproject ĠAg atha , Ġwhere Ġwe Ġdeveloped Ġa Ġpipeline Ġof Ġprocesses Ġthat Ġanalyses Ġtexts Ġ( in ĠPortuguese , ĠSpanish , Ġor ĠEnglish ) Ġand Ġis Ġable Ġto Ġpopulate Ġa Ġspecialized Ġont ology ĠB IB REF 1 Ġ( related Ġto Ġcriminal Ġlaw ) Ġfor Ġthe Ġrepresentation Ġof Ġevents , Ġdepicted Ġin Ġsuch Ġtexts . ĠEvents Ġare Ġrepresented Ġby Ġobjects Ġhaving Ġassociated Ġactions , Ġagents , Ġelements , Ġplaces Ġand Ġtime . ĠAfter Ġhaving Ġpopulated Ġthe Ġevent Ġont ology , Ġwe Ġhave Ġan Ġautomatic Ġprocess Ġlinking Ġthe Ġidentified Ġentities Ġto Ġexternal Ġrefere nt s , Ġcreating , Ġthis Ġway , Ġa Ġlinked Ġdata Ġknowledge Ġbase . </s> It Ġis Ġimportant Ġto Ġpoint Ġout Ġthat , Ġhaving Ġthe Ġtext Ġinformation Ġrepresented Ġin Ġan Ġont ology Ġallows Ġus Ġto Ġperform Ġcomplex Ġqueries Ġand Ġinf erences , Ġwhich Ġcan Ġdetect Ġpatterns Ġof Ġtypical Ġcriminal Ġactions . </s> Another Ġaxe Ġof Ġinnovation Ġin Ġthis Ġresearch Ġis Ġthe Ġdevelopment , Ġfor Ġthe ĠPortuguese Ġlanguage , Ġof Ġa Ġpipeline Ġof ĠNatural ĠLanguage ĠProcessing Ġ( N LP ) Ġprocesses , Ġthat Ġallows Ġus Ġto Ġfully Ġprocess Ġsentences Ġand Ġrepresent Ġtheir Ġcontent Ġin Ġan Ġont ology . ĠAlthough Ġthere Ġare Ġseveral Ġtools Ġfor Ġthe Ġprocessing Ġof Ġthe ĠPortuguese Ġlanguage , Ġthe Ġcombination Ġof Ġall Ġthese Ġsteps Ġin Ġa Ġintegrated Ġtool Ġis Ġa Ġnew Ġcontribution . </s> Moreover , Ġwe Ġhave Ġalready Ġexplored Ġother Ġrelated Ġresearch Ġpath , Ġnamely Ġauthor Ġprofiling ĠB IB REF 2 , Ġaggression Ġidentification ĠB IB REF 3 Ġand Ġhate - speech Ġdetection ĠB IB REF 4 Ġover Ġsocial Ġmedia , Ġplus Ġstatute Ġlaw Ġretrieval Ġand Ġentail ment Ġfor ĠJapanese ĠB IB REF 5 . </s> The Ġremainder Ġof Ġthis Ġpaper Ġis Ġorganized Ġas Ġfollows : ĠSection ĠSEC REF 2 Ġdescribes Ġour Ġproposed Ġarchitecture Ġtogether Ġwith Ġthe ĠPortuguese Ġmodules Ġfor Ġits Ġcomputational Ġprocessing . ĠSection ĠSEC REF 3 Ġdiscusses Ġdifferent Ġdesign Ġoptions Ġand ĠSection ĠSEC REF 4 Ġprovides Ġour Ġconclusions Ġtogether Ġwith Ġsome Ġpointers Ġfor Ġfuture Ġwork . </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText </s> The Ġframework Ġfor Ġprocessing ĠPortuguese Ġtexts Ġis Ġdepicted Ġin ĠFig . ĠFIG REF 2 , Ġwhich Ġillustrates Ġhow Ġrelevant Ġpieces Ġof Ġinformation Ġare Ġextracted Ġfrom Ġthe Ġtext . ĠName ly , Ġinput Ġfiles Ġ( Port ug uese Ġtexts ) Ġgo Ġthrough Ġa Ġseries Ġof Ġmodules : Ġpart - of - speech Ġtagging , Ġnamed Ġentity Ġrecognition , Ġdependency Ġparsing , Ġsemantic Ġrole Ġlabeling , Ġsubject - verb - object Ġtriple Ġextraction , Ġand Ġlex icon Ġmatching . </s> The Ġmain Ġgoal Ġof Ġall Ġthe Ġmodules Ġexcept Ġlex icon Ġmatching Ġis Ġto Ġidentify Ġevents Ġgiven Ġin Ġthe Ġtext . ĠThese Ġevents Ġare Ġthen Ġused Ġto Ġpopulate Ġan Ġont ology . </s> The Ġlex icon Ġmatching , Ġon Ġthe Ġother Ġhand , Ġwas Ġcreated Ġto Ġlink Ġwords Ġthat Ġare Ġfound Ġin Ġthe Ġtext Ġsource Ġwith Ġthe Ġdata Ġavailable Ġnot Ġonly Ġon ĠEuro voc ĠB IB REF 6 Ġthe s aurus Ġbut Ġalso Ġon Ġthe ĠEU 's Ġterminology Ġdatabase ĠI ATE ĠB IB REF 7 Ġ( see ĠSection ĠSEC REF 12 Ġfor Ġdetails ). </s> Most Ġof Ġthese Ġmodules Ġare Ġdeeply Ġrelated Ġand Ġare Ġdetailed Ġin Ġthe Ġsubsequent Ġsubsections . </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠPart - Of - Spe ech ĠT agging </s> Part - of - speech Ġtagging Ġhappens Ġafter Ġlanguage Ġdetection . ĠIt Ġlabels Ġeach Ġword Ġwith Ġa Ġtag Ġthat Ġindicates Ġits Ġsynt actic Ġrole Ġin Ġthe Ġsentence . ĠFor Ġinstance , Ġa Ġword Ġcould Ġbe Ġa Ġnoun , Ġverb , Ġadjective Ġor Ġad verb Ġ( or Ġother Ġsynt actic Ġtag ). ĠWe Ġused ĠFre eling ĠB IB REF 8 Ġlibrary Ġto Ġprovide Ġthe Ġtags . ĠThis Ġlibrary Ġresorts Ġto Ġa ĠHidden ĠMark ov ĠModel Ġas Ġdescribed Ġby ĠBr ants ĠB IB REF 9 . ĠThe Ġend Ġresult Ġis Ġa Ġtag Ġfor Ġeach Ġword Ġas Ġdescribed Ġby Ġthe ĠE AG LES Ġtag set Ġ. </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠNamed ĠEntity ĠRecogn ition </s> We Ġuse Ġthe Ġnamed Ġentity Ġrecognition Ġmodule Ġafter Ġpart - of - speech Ġtagging . ĠThis Ġmodule Ġlabels Ġeach Ġpart Ġof Ġthe Ġsentence Ġinto Ġdifferent Ġcategories Ġsuch Ġas Ġ" P ERSON ", Ġ" LOC ATION ", Ġor Ġ" OR GAN IZ ATION ". ĠWe Ġalso Ġused ĠFre eling Ġto Ġlabel Ġthe Ġnamed Ġentities Ġand Ġthe Ġdetails Ġof Ġthe Ġalgorithm Ġare Ġshown Ġin Ġthe Ġpaper Ġby ĠCar re ras Ġet Ġal ĠB IB REF 10 . ĠAside Ġfrom Ġthe Ġthree Ġaforementioned Ġcategories , Ġwe Ġalso Ġextracted Ġ" D ATE / TIME " Ġand Ġ" C URR ENCY " Ġvalues Ġby Ġlooking Ġat Ġthe Ġpart - of - speech Ġtags : Ġdate / time Ġwords Ġhave Ġa Ġtag Ġof Ġ" W ", Ġwhile Ġcurrencies Ġhave Ġ" Z m ". </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠDepend ency ĠPars ing </s> D epend ency Ġparsing Ġinvolves Ġtagging Ġa Ġword Ġbased Ġon Ġdifferent Ġfeatures Ġto Ġindicate Ġif Ġit Ġis Ġdependent Ġon Ġanother Ġword . ĠThe ĠFre eling Ġlibrary Ġalso Ġhas Ġdependency Ġparsing Ġmodels Ġfor ĠPortuguese . ĠSince Ġwe Ġwanted Ġto Ġbuild Ġa ĠS RL Ġ( Sem antic ĠRole ĠLab eling ) Ġmodule Ġon Ġtop Ġof Ġthe Ġdependency Ġparser Ġand Ġthe Ġcurrent Ġreleased Ġversion Ġof ĠFre eling Ġdoes Ġnot Ġhave Ġan ĠS RL Ġmodule Ġfor ĠPortuguese , Ġwe Ġtrained Ġa Ġdifferent ĠPortuguese Ġdependency Ġparsing Ġmodel Ġthat Ġwas Ġcompatible Ġ( in Ġterms Ġof Ġused Ġtags ) Ġwith Ġthe Ġavailable Ġannot ated . </s> We Ġused Ġthe Ġdataset Ġfrom ĠSystem - T ĠB IB REF 11 , Ġwhich Ġhas ĠS RL Ġtags , Ġas Ġwell Ġas , Ġthe Ġother Ġpreceding Ġtags . ĠIt Ġwas Ġnecessary Ġto Ġdo Ġsome Ġpre - processing Ġand Ġtag Ġmapping Ġin Ġorder Ġto Ġmake Ġit Ġviable Ġto Ġtrain Ġa ĠPortuguese Ġmodel . </s> We Ġmade Ġ5 89 Ġtag Ġconversions Ġover Ġ14 Ġdifferent Ġcategories . ĠThe Ġbreakdown Ġof Ġtag Ġconversions Ġper Ġcategory Ġis Ġgiven Ġby Ġtable ĠT AB REF 7 . ĠThese Ġrules Ġcan Ġbe Ġfurther Ġseen Ġin Ġthe Ġcorresponding ĠGithub Ġrepository ĠB IB REF 12 . ĠThe Ġmodified Ġtraining Ġand Ġdevelopment Ġdatasets Ġare Ġalso Ġavailable Ġon Ġanother ĠGithub Ġrepository page ĠB IB REF 13 Ġfor Ġfurther Ġresearch Ġand Ġcomparison Ġpurposes . </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠSem antic ĠRole ĠLab eling </s> We Ġexecute Ġthe ĠS RL Ġ( Sem antic ĠRole ĠLab eling ) Ġmodule Ġafter Ġobtaining Ġthe Ġword Ġdependencies . ĠThis Ġmodule Ġaims Ġat Ġgiving Ġa Ġsemantic Ġrole Ġto Ġa Ġsynt actic Ġconstituent Ġof Ġa Ġsentence . ĠThe Ġsemantic Ġrole Ġis Ġalways Ġin Ġrelation Ġto Ġa Ġverb Ġand Ġthese Ġroles Ġcould Ġeither Ġbe Ġan Ġactor , Ġobject , Ġtime , Ġor Ġlocation , Ġwhich Ġare Ġthen Ġtagged Ġas ĠA 0 , ĠA 1 , ĠAM - T MP , ĠAM - LOC , Ġrespectively . ĠWe Ġtrained Ġa Ġmodel Ġfor Ġthis Ġmodule Ġon Ġtop Ġof Ġthe Ġdependency Ġparser Ġdescribed Ġin Ġthe Ġprevious Ġsubsection Ġusing Ġthe Ġmodified Ġdataset Ġfrom ĠSystem - T . ĠThe Ġmodule Ġalso Ġneeds Ġco - reference Ġresolution Ġto Ġwork Ġand , Ġto Ġachieve Ġthis , Ġwe Ġadapted Ġthe ĠSpanish Ġco - reference Ġmodules Ġfor ĠPortuguese , Ġchanging Ġthe Ġwords Ġthat Ġare Ġequivalent Ġ( in Ġtotal , Ġwe Ġchanged Ġ253 Ġwords ). </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠSV O ĠExt raction </s> From Ġthe Ġyield Ġof Ġthe ĠS RL Ġ( Sem antic ĠRole ĠLab eling ) Ġmodule , Ġour Ġframework Ġcan Ġdistinguish Ġactors , Ġactions , Ġplaces , Ġtime Ġand Ġobjects Ġfrom Ġthe Ġsentences . ĠUt il izing Ġthis Ġextracted Ġdata , Ġwe Ġcan Ġdistinguish Ġsubject - verb - object Ġ( S VO ) Ġtri ples Ġusing Ġthe ĠSV O Ġextraction Ġalgorithm ĠB IB REF 14 . ĠThe Ġalgorithm Ġfinds , Ġfor Ġeach Ġsentence , Ġthe Ġverb Ġand Ġthe Ġtu ples Ġrelated Ġto Ġthat Ġverb Ġusing ĠSem antic ĠRole ĠLab eling Ġ( sub section ĠSEC REF 8 ). ĠAfter Ġthe Ġextraction Ġof ĠSV Os Ġfrom Ġtexts , Ġthey Ġare Ġinserted Ġinto Ġa Ġspecific Ġevent Ġont ology Ġ( see Ġsection ĠSEC REF 12 Ġfor Ġthe Ġcreation Ġof Ġa Ġknowledge Ġbase ). </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠLex icon ĠMatch ing </s> The Ġsole Ġpurpose Ġof Ġthis Ġmodule Ġis Ġto Ġfind Ġimportant Ġterms Ġand / or Ġconcepts Ġfrom Ġthe Ġextracted Ġtext . ĠTo Ġdo Ġthis , Ġwe Ġuse ĠE uv ov oc ĠB IB REF 6 , Ġa Ġmult ilingual Ġthe s aurus Ġthat Ġwas Ġdeveloped Ġfor Ġand Ġby Ġthe ĠEuropean ĠUnion . ĠThe ĠE uv ov oc Ġhas Ġ21 Ġfields Ġand Ġeach Ġfield Ġis Ġfurther Ġdivided Ġinto Ġa Ġvariable Ġnumber Ġof Ġmicro - t hes auri . ĠHere , Ġdue Ġto Ġthe Ġapplication Ġof Ġthis Ġwork Ġin Ġthe ĠAg atha Ġproject Ġ( mentioned Ġin ĠSection ĠSEC REF 1 ), Ġwe Ġuse Ġthe Ġterms Ġof Ġthe Ġcriminal Ġlaw ĠB IB REF 15 Ġmicro - t hes aurus . ĠFurther , Ġwe Ġclassified Ġeach Ġterm Ġof Ġthe Ġcriminal Ġlaw Ġmicro - t hes aurus Ġinto Ġfour Ġcategories Ġnamely , Ġactor , Ġevent , Ġplace Ġand Ġobject . ĠThe Ġterm Ġclassification Ġcan Ġbe Ġseen Ġin ĠTable ĠT AB REF 11 . </s> After Ġthe Ġclassification Ġof Ġthese Ġterms , Ġwe Ġimplemented Ġtwo Ġdifferent Ġmatching Ġalgorithms Ġbetween Ġthe Ġextracted Ġwords Ġand Ġthe Ġcriminal Ġlaw Ġmicro - t hes aurus Ġterms . ĠThe Ġfirst Ġis Ġan Ġexact Ġstring Ġmatch Ġwherein Ġlower case Ġequivalents Ġof Ġthe Ġwords Ġof Ġthe Ġinput Ġsentences Ġare Ġmatched Ġexactly Ġwith Ġlower Ġcase Ġequivalents Ġof Ġthe Ġpred efined Ġterms . ĠThe Ġsecond Ġmatching Ġalgorithm Ġuses ĠLe ven sh tein Ġdistance ĠB IB REF 16 , Ġallowing Ġsome Ġnear - mat ches Ġthat Ġare Ġclose Ġenough Ġto Ġthe Ġtarget Ġterm . </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠLink ed ĠData : ĠOnt ology , ĠThe s aurus Ġand ĠTermin ology </s> In Ġthe Ġcomputer Ġscience Ġfield , Ġan Ġont ology Ġcan Ġbe Ġdefined Ġhas : </s> a Ġformal Ġspecification Ġof Ġa Ġconceptual ization ; </s> shared Ġvocabulary Ġand Ġtax onomy Ġwhich Ġmodels Ġa Ġdomain Ġwith Ġthe Ġdefinition Ġof Ġobjects Ġand / or Ġconcepts Ġand Ġtheir Ġproperties Ġand Ġrelations ; </s> the Ġrepresentation Ġof Ġentities , Ġideas , Ġand Ġevents , Ġalong Ġwith Ġtheir Ġproperties Ġand Ġrelations , Ġaccording Ġto Ġa Ġsystem Ġof Ġcategories . </s> A Ġknowledge Ġbase Ġis Ġone Ġkind Ġof Ġrepository Ġtypically Ġused Ġto Ġstore Ġanswers Ġto Ġquestions Ġor Ġsolutions Ġto Ġproblems Ġenabling Ġrapid Ġsearch , Ġretrieval , Ġand Ġreuse , Ġeither Ġby Ġan Ġont ology Ġor Ġdirectly Ġby Ġthose Ġrequesting Ġsupport . ĠFor Ġa Ġmore Ġdetailed Ġdescription Ġof Ġont ologies Ġand Ġknowledge Ġbases , Ġsee Ġfor Ġinstance ĠB IB REF 17 . </s> For Ġdesigning Ġthe Ġont ology Ġadequate Ġfor Ġour Ġgoals , Ġwe Ġreferred Ġto Ġthe ĠSimple ĠEvent ĠModel Ġ( S EM ) ĠB IB REF 18 Ġas Ġa Ġbaseline Ġmodel . ĠA Ġpict orial Ġrepresentation Ġof Ġthis Ġont ology Ġis Ġgiven Ġin ĠFigure ĠFIG REF 16 </s> Considering Ġthe Ġcriminal Ġlaw Ġdomain Ġcase Ġstudy , Ġwe Ġmade Ġa Ġfew Ġchanges Ġto Ġthe Ġoriginal ĠSEM Ġont ology . ĠThe Ġentities Ġof Ġthe Ġmodel Ġare : </s> Actor : Ġperson Ġinvolved Ġwith Ġevent </s> Place : Ġlocation Ġof Ġthe Ġevent </s> Time : Ġtime Ġof Ġthe Ġevent </s> Object : Ġthat Ġactor Ġact Ġupon </s> Organ ization : Ġorganization Ġinvolved Ġwith Ġevent </s> C urrency : Ġmoney Ġinvolved Ġwith Ġevent </s> The Ġproposed Ġont ology Ġwas Ġdesigned Ġin Ġsuch Ġa Ġmanner Ġthat Ġit Ġcan Ġincorporate Ġinformation Ġextracted Ġfrom Ġmultiple Ġdocuments . ĠIn Ġthis Ġcontext , Ġsuppose Ġthat Ġthe Ġsource Ġof Ġdocuments Ġis Ġa are Ġa Ġlegal Ġpolice Ġdepartment , Ġwhere Ġeach Ġdocument Ġis are Ġunder Ġthe Ġhood Ġof Ġa Ġparticular Ġcase / crime ; Ġfurthermore Further , Ġa Ġsingle Ġcase Ġcan Ġhave Ġdocuments Ġfrom Ġmultiple Ġlanguages . ĠNow , Ġconsidering Ġcase Ġ1 Ġhas Ġ100 Ġdocuments Ġand Ġcase Ġ2 Ġhas Ġ100 Ġdocuments Ġthen Ġthere Ġis Ġnot Ġonly Ġa Ġconnection Ġamong Ġthe Ġdocuments Ġof Ġa Ġsingle Ġcase Ġbut Ġrather Ġamong Ġall Ġthe Ġcases Ġwith Ġall Ġthe Ġcombined Ġ200 Ġdocuments . ĠIn Ġthis Ġway , Ġthe Ġproposed Ġmethod Ġis Ġable Ġto Ġproduce Ġa Ġdetailed Ġand Ġwell - connected Ġknowledge Ġbase . </s> Figure ĠFIG REF 23 Ġshows Ġthe Ġproposed Ġont ology , Ġwhich , Ġin Ġour Ġevaluation Ġprocedure , Ġwas Ġpopulated Ġwith Ġ3 121 Ġevents Ġentries Ġfrom Ġ51 Ġdocuments . </s> P rote ge ĠB IB REF 19 Ġtool Ġwas Ġused Ġfor Ġcreating Ġthe Ġont ology Ġand ĠGraph DB ĠB IB REF 20 Ġfor Ġpop ulating Ġ& Ġquer ying Ġthe Ġdata . ĠGraph DB Ġis Ġan Ġenterprise - ready ĠSem antic ĠGraph ĠDatabase , Ġcompliant Ġwith ĠW 3 C ĠStandards . ĠSem antic ĠGraph ĠDat abases Ġ( also Ġcalled ĠR DF Ġtri pl est ores ) Ġprovide Ġthe Ġcore Ġinfrastructure Ġfor Ġsolutions Ġwhere Ġmodeling Ġagility , Ġdata Ġintegration , Ġrelationship Ġexploration , Ġand Ġcross - enter prise Ġdata Ġpublishing Ġand Ġconsumption Ġare Ġimportant . ĠGraph DB Ġhas Ġa ĠSP AR QL Ġ( SQL - like Ġquery Ġlanguage ) Ġinterface Ġfor ĠR DF Ġgraph Ġdatabases Ġwith Ġthe Ġfollowing Ġtypes : </s> SELECT : Ġreturns Ġtab ular Ġresults </s> CON STRUCT : Ġcreates Ġa Ġnew ĠR DF Ġgraph Ġbased Ġon Ġquery Ġresults </s> AS K : Ġreturns Ġ" YES ", Ġif Ġthe Ġquery Ġhas Ġa Ġsolution , Ġotherwise Ġ" NO " </s> DES C RI BE : Ġreturns ĠR DF Ġdata Ġabout Ġa Ġresource . ĠThis Ġis Ġuseful Ġwhen Ġthe ĠR DF Ġdata Ġstructure Ġin Ġthe Ġdata Ġsource Ġis Ġnot Ġknown </s> INS ERT : Ġinserts Ġtri ples Ġinto Ġa Ġgraph </s> DE LE TE : Ġdelet es Ġtri ples Ġfrom Ġa Ġgraph </s> Furthermore , Ġwe Ġhave Ġextended Ġthe Ġont ology ĠB IB REF 21 Ġto Ġconnect Ġthe Ġextracted Ġterms Ġwith ĠEuro voc Ġcriminal Ġlaw Ġ( disc ussed Ġin Ġsubsection ĠSEC REF 10 ) Ġand ĠI ATE ĠB IB REF 7 Ġterms . ĠI ATE Ġ( Inter active ĠTermin ology Ġfor ĠEurope ) Ġis Ġthe ĠEU 's Ġgeneral Ġterminology Ġdatabase Ġand Ġits Ġaim Ġis Ġto Ġprovide Ġa Ġweb - based Ġinfrastructure Ġfor Ġall ĠEU Ġterminology Ġresources , Ġenhancing Ġthe Ġavailability Ġand Ġstandard ization Ġof Ġthe Ġinformation . ĠThe Ġextended Ġont ology Ġhas Ġa Ġnumber Ġof Ġsub - classes Ġfor ĠActor , ĠEvent , ĠObject Ġand ĠPlace Ġclasses Ġdetailed Ġin ĠTable ĠT AB REF 30 . </s> Discussion </s> We Ġhave Ġdefined Ġa Ġmajor Ġdesign Ġprinciple Ġfor Ġour Ġarchitecture : Ġit Ġshould Ġbe Ġmodular Ġand Ġnot Ġrely Ġon Ġhuman Ġmade Ġrules Ġallowing , Ġas Ġmuch Ġas Ġpossible , Ġits Ġindependence Ġfrom Ġa Ġspecific Ġlanguage . ĠIn Ġthis Ġway , Ġits Ġpotential Ġapplication Ġto Ġanother Ġlanguage Ġwould Ġbe Ġeasier , Ġsimply Ġby Ġchanging Ġthe Ġmodules Ġor Ġthe Ġmodels Ġof Ġspecific Ġmodules . ĠIn Ġfact , Ġwe Ġhave Ġexplored Ġthe Ġuse Ġof Ġalready Ġexisting Ġmodules Ġand Ġadopted Ġand Ġintegrated Ġseveral Ġof Ġthese Ġtools Ġinto Ġour Ġpipeline . </s> It Ġis Ġimportant Ġto Ġpoint Ġout Ġthat , Ġas Ġfar Ġas Ġwe Ġknow , Ġthere Ġis Ġno Ġintegrated Ġarchitecture Ġsupporting Ġthe Ġfull Ġprocessing Ġpipeline Ġfor Ġthe ĠPortuguese Ġlanguage . ĠWe Ġevaluated Ġseveral Ġsystems Ġlike ĠRem brand t ĠB IB REF 22 Ġor ĠLing ua Kit : Ġthe Ġformer Ġonly Ġhas Ġthe Ġinitial Ġsteps Ġof Ġour Ġproposal Ġ( until ĠN ER ) Ġand Ġthe Ġlater Ġperformed Ġworse Ġthan Ġour Ġsystem . </s> This Ġframework , Ġdeveloped Ġwithin Ġthe Ġcontext Ġof Ġthe ĠAg atha Ġproject Ġ( described Ġin ĠSection ĠSEC REF 1 ) Ġhas Ġthe Ġfull Ġprocessing Ġpipeline Ġfor ĠPortuguese Ġtexts : Ġit Ġreceives Ġsentences Ġas Ġinput Ġand Ġoutputs Ġont ological Ġinformation : Ġa ) Ġfirst Ġperforms Ġall ĠN LP Ġtypical Ġtasks Ġuntil Ġsemantic Ġrole Ġlab elling ; Ġb ) Ġthen , Ġit Ġextracts Ġsubject - verb - object Ġtri ples ; Ġc ) Ġand , Ġthen , Ġit Ġperforms Ġont ology Ġmatching Ġprocedures . ĠAs Ġa Ġfinal Ġresult , Ġthe Ġobtained Ġoutput Ġis Ġinserted Ġinto Ġa Ġspecialized Ġont ology . </s> We Ġare Ġaware Ġthat Ġeach Ġof Ġthe Ġarchitecture Ġmodules Ġcan , Ġand Ġshould , Ġbe Ġimproved Ġbut Ġour Ġmain Ġgoal Ġwas Ġthe Ġcreation Ġof Ġa Ġfull Ġworking Ġtext Ġprocessing Ġpipeline Ġfor Ġthe ĠPortuguese Ġlanguage . </s> Con clusions Ġand ĠFuture ĠWork </s> Besides Ġthe Ġend âĢĵ to âĢĵ end ĠN LP Ġpipeline Ġfor Ġthe ĠPortuguese Ġlanguage , Ġthe Ġother Ġmain Ġcontributions Ġof Ġthis Ġwork Ġcan Ġbe Ġsummarize Ġas Ġfollows : </s> Development Ġof Ġan Ġont ology Ġfor Ġthe Ġcriminal Ġlaw Ġdomain ; </s> Al ignment Ġof Ġthe ĠEuro voc Ġthe s aurus Ġand ĠI ATE Ġterminology Ġwith Ġthe Ġont ology Ġcreated ; </s> Represent ation Ġof Ġthe Ġextracted Ġevents Ġfrom Ġtexts Ġin Ġthe Ġlinked Ġknowledge Ġbase Ġdefined . </s> The Ġobtained Ġresults Ġsupport Ġour Ġclaim Ġthat Ġthe Ġproposed Ġsystem Ġcan Ġbe Ġused Ġas Ġa Ġbase Ġtool Ġfor Ġinformation Ġextraction Ġfor Ġthe ĠPortuguese Ġlanguage . ĠBeing Ġcomposed Ġby Ġseveral Ġmodules , Ġeach Ġof Ġthem Ġwith Ġa Ġhigh Ġlevel Ġof Ġcomplexity , Ġit Ġis Ġcertain Ġthat Ġour Ġapproach Ġcan Ġbe Ġimproved Ġand Ġan Ġoverall Ġbetter Ġperformance Ġcan Ġbe Ġachieved . </s> As Ġfuture Ġwork Ġwe Ġintend , Ġnot Ġonly Ġto Ġcontinue Ġimproving Ġthe Ġindividual Ġmodules , Ġbut Ġalso Ġplan Ġto Ġextend Ġthis Ġwork Ġto Ġthe : </s> automatic Ġcreation Ġof Ġevent Ġtimelines ; </s> inc orpor ation Ġin Ġthe Ġknowledge Ġbase Ġof Ġinformation Ġobtained Ġfrom Ġvideos Ġor Ġpictures Ġdescribing Ġscenes Ġrelevant Ġto Ġcriminal Ġinvestigations . </s> Acknowled gments </s> The Ġauthors Ġwould Ġlike Ġto Ġthank ĠCOMP ET E Ġ2020 , ĠP ORT UG AL Ġ2020 ĠProgram , Ġthe ĠEuropean ĠUnion , Ġand ĠAL ENT E JO Ġ2020 Ġfor Ġsupporting Ġthis Ġresearch Ġas Ġpart Ġof ĠAg atha ĠProject ĠSI Ġ& ĠID T Ġnumber Ġ180 22 Ġ( Int elligent Ġanalysis Ġsystem Ġof Ġopen Ġof Ġsources Ġinformation Ġfor Ġsurveillance / crime Ġcontrol ). ĠThe Ġauthors Ġwould Ġalso Ġlike Ġto Ġthank ĠL IS P Ġ- ĠLaboratory Ġof ĠIn format ics , ĠSystems Ġand ĠParallel ism .
Introduction The automatic identification, extraction and representation of the information conveyed in texts is a key task nowadays. In fact, this research topic is increasing its relevance with the exponential growth of social networks and the need to have tools that are able to automatically process them BIBREF0. Some of the domains where it is more important to be able to perform this kind of action are the juridical and legal ones. Effectively, it is crucial to have the capability to analyse open access text sources, like social nets (Twitter and Facebook, for instance), blogs, online newspapers, and to be able to extract the relevant information and represent it in a knowledge base, allowing posterior inferences and reasoning. In the context of this work, we will present results of the R&D project Agatha, where we developed a pipeline of processes that analyses texts (in Portuguese, Spanish, or English) and is able to populate a specialized ontology BIBREF1 (related to criminal law) for the representation of events, depicted in such texts. Events are represented by objects having associated actions, agents, elements, places and time. After having populated the event ontology, we have an automatic process linking the identified entities to external referents, creating, this way, a linked data knowledge base. It is important to point out that, having the text information represented in an ontology allows us to perform complex queries and inferences, which can detect patterns of typical criminal actions. Another axe of innovation in this research is the development, for the Portuguese language, of a pipeline of Natural Language Processing (NLP) processes, that allows us to fully process sentences and represent their content in an ontology. Although there are several tools for the processing of the Portuguese language, the combination of all these steps in a integrated tool is a new contribution. Moreover, we have already explored other related research path, namely author profiling BIBREF2, aggression identification BIBREF3 and hate-speech detection BIBREF4 over social media, plus statute law retrieval and entailment for Japanese BIBREF5. The remainder of this paper is organized as follows: Section SECREF2 describes our proposed architecture together with the Portuguese modules for its computational processing. Section SECREF3 discusses different design options and Section SECREF4 provides our conclusions together with some pointers for future work. Framework for Processing Portuguese Text The framework for processing Portuguese texts is depicted in Fig. FIGREF2, which illustrates how relevant pieces of information are extracted from the text. Namely, input files (Portuguese texts) go through a series of modules: part-of-speech tagging, named entity recognition, dependency parsing, semantic role labeling, subject-verb-object triple extraction, and lexicon matching. The main goal of all the modules except lexicon matching is to identify events given in the text. These events are then used to populate an ontology. The lexicon matching, on the other hand, was created to link words that are found in the text source with the data available not only on Eurovoc BIBREF6 thesaurus but also on the EU's terminology database IATE BIBREF7 (see Section SECREF12 for details). Most of these modules are deeply related and are detailed in the subsequent subsections. Framework for Processing Portuguese Text ::: Part-Of-Speech Tagging Part-of-speech tagging happens after language detection. It labels each word with a tag that indicates its syntactic role in the sentence. For instance, a word could be a noun, verb, adjective or adverb (or other syntactic tag). We used Freeling BIBREF8 library to provide the tags. This library resorts to a Hidden Markov Model as described by Brants BIBREF9. The end result is a tag for each word as described by the EAGLES tagset . Framework for Processing Portuguese Text ::: Named Entity Recognition We use the named entity recognition module after part-of-speech tagging. This module labels each part of the sentence into different categories such as "PERSON", "LOCATION", or "ORGANIZATION". We also used Freeling to label the named entities and the details of the algorithm are shown in the paper by Carreras et al BIBREF10. Aside from the three aforementioned categories, we also extracted "DATE/TIME" and "CURRENCY" values by looking at the part-of-speech tags: date/time words have a tag of "W", while currencies have "Zm". Framework for Processing Portuguese Text ::: Dependency Parsing Dependency parsing involves tagging a word based on different features to indicate if it is dependent on another word. The Freeling library also has dependency parsing models for Portuguese. Since we wanted to build a SRL (Semantic Role Labeling) module on top of the dependency parser and the current released version of Freeling does not have an SRL module for Portuguese, we trained a different Portuguese dependency parsing model that was compatible (in terms of used tags) with the available annotated. We used the dataset from System-T BIBREF11, which has SRL tags, as well as, the other preceding tags. It was necessary to do some pre-processing and tag mapping in order to make it viable to train a Portuguese model. We made 589 tag conversions over 14 different categories. The breakdown of tag conversions per category is given by table TABREF7. These rules can be further seen in the corresponding Github repository BIBREF12. The modified training and development datasets are also available on another Github repositorypage BIBREF13 for further research and comparison purposes. Framework for Processing Portuguese Text ::: Semantic Role Labeling We execute the SRL (Semantic Role Labeling) module after obtaining the word dependencies. This module aims at giving a semantic role to a syntactic constituent of a sentence. The semantic role is always in relation to a verb and these roles could either be an actor, object, time, or location, which are then tagged as A0, A1, AM-TMP, AM-LOC, respectively. We trained a model for this module on top of the dependency parser described in the previous subsection using the modified dataset from System-T. The module also needs co-reference resolution to work and, to achieve this, we adapted the Spanish co-reference modules for Portuguese, changing the words that are equivalent (in total, we changed 253 words). Framework for Processing Portuguese Text ::: SVO Extraction From the yield of the SRL (Semantic Role Labeling) module, our framework can distinguish actors, actions, places, time and objects from the sentences. Utilizing this extracted data, we can distinguish subject-verb-object (SVO) triples using the SVO extraction algorithm BIBREF14. The algorithm finds, for each sentence, the verb and the tuples related to that verb using Semantic Role Labeling (subsection SECREF8). After the extraction of SVOs from texts, they are inserted into a specific event ontology (see section SECREF12 for the creation of a knowledge base). Framework for Processing Portuguese Text ::: Lexicon Matching The sole purpose of this module is to find important terms and/or concepts from the extracted text. To do this, we use Euvovoc BIBREF6, a multilingual thesaurus that was developed for and by the European Union. The Euvovoc has 21 fields and each field is further divided into a variable number of micro-thesauri. Here, due to the application of this work in the Agatha project (mentioned in Section SECREF1), we use the terms of the criminal law BIBREF15 micro-thesaurus. Further, we classified each term of the criminal law micro-thesaurus into four categories namely, actor, event, place and object. The term classification can be seen in Table TABREF11. After the classification of these terms, we implemented two different matching algorithms between the extracted words and the criminal law micro-thesaurus terms. The first is an exact string match wherein lowercase equivalents of the words of the input sentences are matched exactly with lower case equivalents of the predefined terms. The second matching algorithm uses Levenshtein distance BIBREF16, allowing some near-matches that are close enough to the target term. Framework for Processing Portuguese Text ::: Linked Data: Ontology, Thesaurus and Terminology In the computer science field, an ontology can be defined has: a formal specification of a conceptualization; shared vocabulary and taxonomy which models a domain with the definition of objects and/or concepts and their properties and relations; the representation of entities, ideas, and events, along with their properties and relations, according to a system of categories. A knowledge base is one kind of repository typically used to store answers to questions or solutions to problems enabling rapid search, retrieval, and reuse, either by an ontology or directly by those requesting support. For a more detailed description of ontologies and knowledge bases, see for instance BIBREF17. For designing the ontology adequate for our goals, we referred to the Simple Event Model (SEM) BIBREF18 as a baseline model. A pictorial representation of this ontology is given in Figure FIGREF16 Considering the criminal law domain case study, we made a few changes to the original SEM ontology. The entities of the model are: Actor: person involved with event Place: location of the event Time: time of the event Object: that actor act upon Organization: organization involved with event Currency: money involved with event The proposed ontology was designed in such a manner that it can incorporate information extracted from multiple documents. In this context, suppose that the source of documents is aare a legal police department, where each document isare under the hood of a particular case/crime; furthermoreFurther, a single case can have documents from multiple languages. Now, considering case 1 has 100 documents and case 2 has 100 documents then there is not only a connection among the documents of a single case but rather among all the cases with all the combined 200 documents. In this way, the proposed method is able to produce a detailed and well-connected knowledge base. Figure FIGREF23 shows the proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents. Protege BIBREF19 tool was used for creating the ontology and GraphDB BIBREF20 for populating & querying the data. GraphDB is an enterprise-ready Semantic Graph Database, compliant with W3C Standards. Semantic Graph Databases (also called RDF triplestores) provide the core infrastructure for solutions where modeling agility, data integration, relationship exploration, and cross-enterprise data publishing and consumption are important. GraphDB has a SPARQL (SQL-like query language) interface for RDF graph databases with the following types: SELECT: returns tabular results CONSTRUCT: creates a new RDF graph based on query results ASK: returns "YES", if the query has a solution, otherwise "NO" DESCRIBE: returns RDF data about a resource. This is useful when the RDF data structure in the data source is not known INSERT: inserts triples into a graph DELETE: deletes triples from a graph Furthermore, we have extended the ontology BIBREF21 to connect the extracted terms with Eurovoc criminal law (discussed in subsection SECREF10) and IATE BIBREF7 terms. IATE (Interactive Terminology for Europe) is the EU's general terminology database and its aim is to provide a web-based infrastructure for all EU terminology resources, enhancing the availability and standardization of the information. The extended ontology has a number of sub-classes for Actor, Event, Object and Place classes detailed in Table TABREF30. Discussion We have defined a major design principle for our architecture: it should be modular and not rely on human made rules allowing, as much as possible, its independence from a specific language. In this way, its potential application to another language would be easier, simply by changing the modules or the models of specific modules. In fact, we have explored the use of already existing modules and adopted and integrated several of these tools into our pipeline. It is important to point out that, as far as we know, there is no integrated architecture supporting the full processing pipeline for the Portuguese language. We evaluated several systems like Rembrandt BIBREF22 or LinguaKit: the former only has the initial steps of our proposal (until NER) and the later performed worse than our system. This framework, developed within the context of the Agatha project (described in Section SECREF1) has the full processing pipeline for Portuguese texts: it receives sentences as input and outputs ontological information: a) first performs all NLP typical tasks until semantic role labelling; b) then, it extracts subject-verb-object triples; c) and, then, it performs ontology matching procedures. As a final result, the obtained output is inserted into a specialized ontology. We are aware that each of the architecture modules can, and should, be improved but our main goal was the creation of a full working text processing pipeline for the Portuguese language. Conclusions and Future Work Besides the end–to–end NLP pipeline for the Portuguese language, the other main contributions of this work can be summarize as follows: Development of an ontology for the criminal law domain; Alignment of the Eurovoc thesaurus and IATE terminology with the ontology created; Representation of the extracted events from texts in the linked knowledge base defined. The obtained results support our claim that the proposed system can be used as a base tool for information extraction for the Portuguese language. Being composed by several modules, each of them with a high level of complexity, it is certain that our approach can be improved and an overall better performance can be achieved. As future work we intend, not only to continue improving the individual modules, but also plan to extend this work to the: automatic creation of event timelines; incorporation in the knowledge base of information obtained from videos or pictures describing scenes relevant to criminal investigations. Acknowledgments The authors would like to thank COMPETE 2020, PORTUGAL 2020 Program, the European Union, and ALENTEJO 2020 for supporting this research as part of Agatha Project SI & IDT number 18022 (Intelligent analysis system of open of sources information for surveillance/crime control). The authors would also like to thank LISP - Laboratory of Informatics, Systems and Parallelism.
Were any of the pipeline components based on deep learning models?
8b0abc1907c2bf3e0256f8cf85e0ba66a839bd92
Were Ġany Ġof Ġthe Ġpipeline Ġcomponents Ġbased Ġon Ġdeep Ġlearning Ġmodels ?
No
<s> Were Ġany Ġof Ġthe Ġpipeline Ġcomponents Ġbased Ġon Ġdeep Ġlearning Ġmodels ? </s> Introduction </s> The Ġautomatic Ġidentification , Ġextraction Ġand Ġrepresentation Ġof Ġthe Ġinformation Ġconveyed Ġin Ġtexts Ġis Ġa Ġkey Ġtask Ġnowadays . ĠIn Ġfact , Ġthis Ġresearch Ġtopic Ġis Ġincreasing Ġits Ġrelevance Ġwith Ġthe Ġexponential Ġgrowth Ġof Ġsocial Ġnetworks Ġand Ġthe Ġneed Ġto Ġhave Ġtools Ġthat Ġare Ġable Ġto Ġautomatically Ġprocess Ġthem ĠB IB REF 0 . </s> Some Ġof Ġthe Ġdomains Ġwhere Ġit Ġis Ġmore Ġimportant Ġto Ġbe Ġable Ġto Ġperform Ġthis Ġkind Ġof Ġaction Ġare Ġthe Ġjur id ical Ġand Ġlegal Ġones . ĠEffect ively , Ġit Ġis Ġcrucial Ġto Ġhave Ġthe Ġcapability Ġto Ġanalyse Ġopen Ġaccess Ġtext Ġsources , Ġlike Ġsocial Ġnets Ġ( Twitter Ġand ĠFacebook , Ġfor Ġinstance ), Ġblogs , Ġonline Ġnewspapers , Ġand Ġto Ġbe Ġable Ġto Ġextract Ġthe Ġrelevant Ġinformation Ġand Ġrepresent Ġit Ġin Ġa Ġknowledge Ġbase , Ġallowing Ġposterior Ġinf erences Ġand Ġreasoning . </s> In Ġthe Ġcontext Ġof Ġthis Ġwork , Ġwe Ġwill Ġpresent Ġresults Ġof Ġthe ĠR & D Ġproject ĠAg atha , Ġwhere Ġwe Ġdeveloped Ġa Ġpipeline Ġof Ġprocesses Ġthat Ġanalyses Ġtexts Ġ( in ĠPortuguese , ĠSpanish , Ġor ĠEnglish ) Ġand Ġis Ġable Ġto Ġpopulate Ġa Ġspecialized Ġont ology ĠB IB REF 1 Ġ( related Ġto Ġcriminal Ġlaw ) Ġfor Ġthe Ġrepresentation Ġof Ġevents , Ġdepicted Ġin Ġsuch Ġtexts . ĠEvents Ġare Ġrepresented Ġby Ġobjects Ġhaving Ġassociated Ġactions , Ġagents , Ġelements , Ġplaces Ġand Ġtime . ĠAfter Ġhaving Ġpopulated Ġthe Ġevent Ġont ology , Ġwe Ġhave Ġan Ġautomatic Ġprocess Ġlinking Ġthe Ġidentified Ġentities Ġto Ġexternal Ġrefere nt s , Ġcreating , Ġthis Ġway , Ġa Ġlinked Ġdata Ġknowledge Ġbase . </s> It Ġis Ġimportant Ġto Ġpoint Ġout Ġthat , Ġhaving Ġthe Ġtext Ġinformation Ġrepresented Ġin Ġan Ġont ology Ġallows Ġus Ġto Ġperform Ġcomplex Ġqueries Ġand Ġinf erences , Ġwhich Ġcan Ġdetect Ġpatterns Ġof Ġtypical Ġcriminal Ġactions . </s> Another Ġaxe Ġof Ġinnovation Ġin Ġthis Ġresearch Ġis Ġthe Ġdevelopment , Ġfor Ġthe ĠPortuguese Ġlanguage , Ġof Ġa Ġpipeline Ġof ĠNatural ĠLanguage ĠProcessing Ġ( N LP ) Ġprocesses , Ġthat Ġallows Ġus Ġto Ġfully Ġprocess Ġsentences Ġand Ġrepresent Ġtheir Ġcontent Ġin Ġan Ġont ology . ĠAlthough Ġthere Ġare Ġseveral Ġtools Ġfor Ġthe Ġprocessing Ġof Ġthe ĠPortuguese Ġlanguage , Ġthe Ġcombination Ġof Ġall Ġthese Ġsteps Ġin Ġa Ġintegrated Ġtool Ġis Ġa Ġnew Ġcontribution . </s> Moreover , Ġwe Ġhave Ġalready Ġexplored Ġother Ġrelated Ġresearch Ġpath , Ġnamely Ġauthor Ġprofiling ĠB IB REF 2 , Ġaggression Ġidentification ĠB IB REF 3 Ġand Ġhate - speech Ġdetection ĠB IB REF 4 Ġover Ġsocial Ġmedia , Ġplus Ġstatute Ġlaw Ġretrieval Ġand Ġentail ment Ġfor ĠJapanese ĠB IB REF 5 . </s> The Ġremainder Ġof Ġthis Ġpaper Ġis Ġorganized Ġas Ġfollows : ĠSection ĠSEC REF 2 Ġdescribes Ġour Ġproposed Ġarchitecture Ġtogether Ġwith Ġthe ĠPortuguese Ġmodules Ġfor Ġits Ġcomputational Ġprocessing . ĠSection ĠSEC REF 3 Ġdiscusses Ġdifferent Ġdesign Ġoptions Ġand ĠSection ĠSEC REF 4 Ġprovides Ġour Ġconclusions Ġtogether Ġwith Ġsome Ġpointers Ġfor Ġfuture Ġwork . </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText </s> The Ġframework Ġfor Ġprocessing ĠPortuguese Ġtexts Ġis Ġdepicted Ġin ĠFig . ĠFIG REF 2 , Ġwhich Ġillustrates Ġhow Ġrelevant Ġpieces Ġof Ġinformation Ġare Ġextracted Ġfrom Ġthe Ġtext . ĠName ly , Ġinput Ġfiles Ġ( Port ug uese Ġtexts ) Ġgo Ġthrough Ġa Ġseries Ġof Ġmodules : Ġpart - of - speech Ġtagging , Ġnamed Ġentity Ġrecognition , Ġdependency Ġparsing , Ġsemantic Ġrole Ġlabeling , Ġsubject - verb - object Ġtriple Ġextraction , Ġand Ġlex icon Ġmatching . </s> The Ġmain Ġgoal Ġof Ġall Ġthe Ġmodules Ġexcept Ġlex icon Ġmatching Ġis Ġto Ġidentify Ġevents Ġgiven Ġin Ġthe Ġtext . ĠThese Ġevents Ġare Ġthen Ġused Ġto Ġpopulate Ġan Ġont ology . </s> The Ġlex icon Ġmatching , Ġon Ġthe Ġother Ġhand , Ġwas Ġcreated Ġto Ġlink Ġwords Ġthat Ġare Ġfound Ġin Ġthe Ġtext Ġsource Ġwith Ġthe Ġdata Ġavailable Ġnot Ġonly Ġon ĠEuro voc ĠB IB REF 6 Ġthe s aurus Ġbut Ġalso Ġon Ġthe ĠEU 's Ġterminology Ġdatabase ĠI ATE ĠB IB REF 7 Ġ( see ĠSection ĠSEC REF 12 Ġfor Ġdetails ). </s> Most Ġof Ġthese Ġmodules Ġare Ġdeeply Ġrelated Ġand Ġare Ġdetailed Ġin Ġthe Ġsubsequent Ġsubsections . </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠPart - Of - Spe ech ĠT agging </s> Part - of - speech Ġtagging Ġhappens Ġafter Ġlanguage Ġdetection . ĠIt Ġlabels Ġeach Ġword Ġwith Ġa Ġtag Ġthat Ġindicates Ġits Ġsynt actic Ġrole Ġin Ġthe Ġsentence . ĠFor Ġinstance , Ġa Ġword Ġcould Ġbe Ġa Ġnoun , Ġverb , Ġadjective Ġor Ġad verb Ġ( or Ġother Ġsynt actic Ġtag ). ĠWe Ġused ĠFre eling ĠB IB REF 8 Ġlibrary Ġto Ġprovide Ġthe Ġtags . ĠThis Ġlibrary Ġresorts Ġto Ġa ĠHidden ĠMark ov ĠModel Ġas Ġdescribed Ġby ĠBr ants ĠB IB REF 9 . ĠThe Ġend Ġresult Ġis Ġa Ġtag Ġfor Ġeach Ġword Ġas Ġdescribed Ġby Ġthe ĠE AG LES Ġtag set Ġ. </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠNamed ĠEntity ĠRecogn ition </s> We Ġuse Ġthe Ġnamed Ġentity Ġrecognition Ġmodule Ġafter Ġpart - of - speech Ġtagging . ĠThis Ġmodule Ġlabels Ġeach Ġpart Ġof Ġthe Ġsentence Ġinto Ġdifferent Ġcategories Ġsuch Ġas Ġ" P ERSON ", Ġ" LOC ATION ", Ġor Ġ" OR GAN IZ ATION ". ĠWe Ġalso Ġused ĠFre eling Ġto Ġlabel Ġthe Ġnamed Ġentities Ġand Ġthe Ġdetails Ġof Ġthe Ġalgorithm Ġare Ġshown Ġin Ġthe Ġpaper Ġby ĠCar re ras Ġet Ġal ĠB IB REF 10 . ĠAside Ġfrom Ġthe Ġthree Ġaforementioned Ġcategories , Ġwe Ġalso Ġextracted Ġ" D ATE / TIME " Ġand Ġ" C URR ENCY " Ġvalues Ġby Ġlooking Ġat Ġthe Ġpart - of - speech Ġtags : Ġdate / time Ġwords Ġhave Ġa Ġtag Ġof Ġ" W ", Ġwhile Ġcurrencies Ġhave Ġ" Z m ". </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠDepend ency ĠPars ing </s> D epend ency Ġparsing Ġinvolves Ġtagging Ġa Ġword Ġbased Ġon Ġdifferent Ġfeatures Ġto Ġindicate Ġif Ġit Ġis Ġdependent Ġon Ġanother Ġword . ĠThe ĠFre eling Ġlibrary Ġalso Ġhas Ġdependency Ġparsing Ġmodels Ġfor ĠPortuguese . ĠSince Ġwe Ġwanted Ġto Ġbuild Ġa ĠS RL Ġ( Sem antic ĠRole ĠLab eling ) Ġmodule Ġon Ġtop Ġof Ġthe Ġdependency Ġparser Ġand Ġthe Ġcurrent Ġreleased Ġversion Ġof ĠFre eling Ġdoes Ġnot Ġhave Ġan ĠS RL Ġmodule Ġfor ĠPortuguese , Ġwe Ġtrained Ġa Ġdifferent ĠPortuguese Ġdependency Ġparsing Ġmodel Ġthat Ġwas Ġcompatible Ġ( in Ġterms Ġof Ġused Ġtags ) Ġwith Ġthe Ġavailable Ġannot ated . </s> We Ġused Ġthe Ġdataset Ġfrom ĠSystem - T ĠB IB REF 11 , Ġwhich Ġhas ĠS RL Ġtags , Ġas Ġwell Ġas , Ġthe Ġother Ġpreceding Ġtags . ĠIt Ġwas Ġnecessary Ġto Ġdo Ġsome Ġpre - processing Ġand Ġtag Ġmapping Ġin Ġorder Ġto Ġmake Ġit Ġviable Ġto Ġtrain Ġa ĠPortuguese Ġmodel . </s> We Ġmade Ġ5 89 Ġtag Ġconversions Ġover Ġ14 Ġdifferent Ġcategories . ĠThe Ġbreakdown Ġof Ġtag Ġconversions Ġper Ġcategory Ġis Ġgiven Ġby Ġtable ĠT AB REF 7 . ĠThese Ġrules Ġcan Ġbe Ġfurther Ġseen Ġin Ġthe Ġcorresponding ĠGithub Ġrepository ĠB IB REF 12 . ĠThe Ġmodified Ġtraining Ġand Ġdevelopment Ġdatasets Ġare Ġalso Ġavailable Ġon Ġanother ĠGithub Ġrepository page ĠB IB REF 13 Ġfor Ġfurther Ġresearch Ġand Ġcomparison Ġpurposes . </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠSem antic ĠRole ĠLab eling </s> We Ġexecute Ġthe ĠS RL Ġ( Sem antic ĠRole ĠLab eling ) Ġmodule Ġafter Ġobtaining Ġthe Ġword Ġdependencies . ĠThis Ġmodule Ġaims Ġat Ġgiving Ġa Ġsemantic Ġrole Ġto Ġa Ġsynt actic Ġconstituent Ġof Ġa Ġsentence . ĠThe Ġsemantic Ġrole Ġis Ġalways Ġin Ġrelation Ġto Ġa Ġverb Ġand Ġthese Ġroles Ġcould Ġeither Ġbe Ġan Ġactor , Ġobject , Ġtime , Ġor Ġlocation , Ġwhich Ġare Ġthen Ġtagged Ġas ĠA 0 , ĠA 1 , ĠAM - T MP , ĠAM - LOC , Ġrespectively . ĠWe Ġtrained Ġa Ġmodel Ġfor Ġthis Ġmodule Ġon Ġtop Ġof Ġthe Ġdependency Ġparser Ġdescribed Ġin Ġthe Ġprevious Ġsubsection Ġusing Ġthe Ġmodified Ġdataset Ġfrom ĠSystem - T . ĠThe Ġmodule Ġalso Ġneeds Ġco - reference Ġresolution Ġto Ġwork Ġand , Ġto Ġachieve Ġthis , Ġwe Ġadapted Ġthe ĠSpanish Ġco - reference Ġmodules Ġfor ĠPortuguese , Ġchanging Ġthe Ġwords Ġthat Ġare Ġequivalent Ġ( in Ġtotal , Ġwe Ġchanged Ġ253 Ġwords ). </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠSV O ĠExt raction </s> From Ġthe Ġyield Ġof Ġthe ĠS RL Ġ( Sem antic ĠRole ĠLab eling ) Ġmodule , Ġour Ġframework Ġcan Ġdistinguish Ġactors , Ġactions , Ġplaces , Ġtime Ġand Ġobjects Ġfrom Ġthe Ġsentences . ĠUt il izing Ġthis Ġextracted Ġdata , Ġwe Ġcan Ġdistinguish Ġsubject - verb - object Ġ( S VO ) Ġtri ples Ġusing Ġthe ĠSV O Ġextraction Ġalgorithm ĠB IB REF 14 . ĠThe Ġalgorithm Ġfinds , Ġfor Ġeach Ġsentence , Ġthe Ġverb Ġand Ġthe Ġtu ples Ġrelated Ġto Ġthat Ġverb Ġusing ĠSem antic ĠRole ĠLab eling Ġ( sub section ĠSEC REF 8 ). ĠAfter Ġthe Ġextraction Ġof ĠSV Os Ġfrom Ġtexts , Ġthey Ġare Ġinserted Ġinto Ġa Ġspecific Ġevent Ġont ology Ġ( see Ġsection ĠSEC REF 12 Ġfor Ġthe Ġcreation Ġof Ġa Ġknowledge Ġbase ). </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠLex icon ĠMatch ing </s> The Ġsole Ġpurpose Ġof Ġthis Ġmodule Ġis Ġto Ġfind Ġimportant Ġterms Ġand / or Ġconcepts Ġfrom Ġthe Ġextracted Ġtext . ĠTo Ġdo Ġthis , Ġwe Ġuse ĠE uv ov oc ĠB IB REF 6 , Ġa Ġmult ilingual Ġthe s aurus Ġthat Ġwas Ġdeveloped Ġfor Ġand Ġby Ġthe ĠEuropean ĠUnion . ĠThe ĠE uv ov oc Ġhas Ġ21 Ġfields Ġand Ġeach Ġfield Ġis Ġfurther Ġdivided Ġinto Ġa Ġvariable Ġnumber Ġof Ġmicro - t hes auri . ĠHere , Ġdue Ġto Ġthe Ġapplication Ġof Ġthis Ġwork Ġin Ġthe ĠAg atha Ġproject Ġ( mentioned Ġin ĠSection ĠSEC REF 1 ), Ġwe Ġuse Ġthe Ġterms Ġof Ġthe Ġcriminal Ġlaw ĠB IB REF 15 Ġmicro - t hes aurus . ĠFurther , Ġwe Ġclassified Ġeach Ġterm Ġof Ġthe Ġcriminal Ġlaw Ġmicro - t hes aurus Ġinto Ġfour Ġcategories Ġnamely , Ġactor , Ġevent , Ġplace Ġand Ġobject . ĠThe Ġterm Ġclassification Ġcan Ġbe Ġseen Ġin ĠTable ĠT AB REF 11 . </s> After Ġthe Ġclassification Ġof Ġthese Ġterms , Ġwe Ġimplemented Ġtwo Ġdifferent Ġmatching Ġalgorithms Ġbetween Ġthe Ġextracted Ġwords Ġand Ġthe Ġcriminal Ġlaw Ġmicro - t hes aurus Ġterms . ĠThe Ġfirst Ġis Ġan Ġexact Ġstring Ġmatch Ġwherein Ġlower case Ġequivalents Ġof Ġthe Ġwords Ġof Ġthe Ġinput Ġsentences Ġare Ġmatched Ġexactly Ġwith Ġlower Ġcase Ġequivalents Ġof Ġthe Ġpred efined Ġterms . ĠThe Ġsecond Ġmatching Ġalgorithm Ġuses ĠLe ven sh tein Ġdistance ĠB IB REF 16 , Ġallowing Ġsome Ġnear - mat ches Ġthat Ġare Ġclose Ġenough Ġto Ġthe Ġtarget Ġterm . </s> Fram ework Ġfor ĠProcessing ĠPortuguese ĠText Ġ: :: ĠLink ed ĠData : ĠOnt ology , ĠThe s aurus Ġand ĠTermin ology </s> In Ġthe Ġcomputer Ġscience Ġfield , Ġan Ġont ology Ġcan Ġbe Ġdefined Ġhas : </s> a Ġformal Ġspecification Ġof Ġa Ġconceptual ization ; </s> shared Ġvocabulary Ġand Ġtax onomy Ġwhich Ġmodels Ġa Ġdomain Ġwith Ġthe Ġdefinition Ġof Ġobjects Ġand / or Ġconcepts Ġand Ġtheir Ġproperties Ġand Ġrelations ; </s> the Ġrepresentation Ġof Ġentities , Ġideas , Ġand Ġevents , Ġalong Ġwith Ġtheir Ġproperties Ġand Ġrelations , Ġaccording Ġto Ġa Ġsystem Ġof Ġcategories . </s> A Ġknowledge Ġbase Ġis Ġone Ġkind Ġof Ġrepository Ġtypically Ġused Ġto Ġstore Ġanswers Ġto Ġquestions Ġor Ġsolutions Ġto Ġproblems Ġenabling Ġrapid Ġsearch , Ġretrieval , Ġand Ġreuse , Ġeither Ġby Ġan Ġont ology Ġor Ġdirectly Ġby Ġthose Ġrequesting Ġsupport . ĠFor Ġa Ġmore Ġdetailed Ġdescription Ġof Ġont ologies Ġand Ġknowledge Ġbases , Ġsee Ġfor Ġinstance ĠB IB REF 17 . </s> For Ġdesigning Ġthe Ġont ology Ġadequate Ġfor Ġour Ġgoals , Ġwe Ġreferred Ġto Ġthe ĠSimple ĠEvent ĠModel Ġ( S EM ) ĠB IB REF 18 Ġas Ġa Ġbaseline Ġmodel . ĠA Ġpict orial Ġrepresentation Ġof Ġthis Ġont ology Ġis Ġgiven Ġin ĠFigure ĠFIG REF 16 </s> Considering Ġthe Ġcriminal Ġlaw Ġdomain Ġcase Ġstudy , Ġwe Ġmade Ġa Ġfew Ġchanges Ġto Ġthe Ġoriginal ĠSEM Ġont ology . ĠThe Ġentities Ġof Ġthe Ġmodel Ġare : </s> Actor : Ġperson Ġinvolved Ġwith Ġevent </s> Place : Ġlocation Ġof Ġthe Ġevent </s> Time : Ġtime Ġof Ġthe Ġevent </s> Object : Ġthat Ġactor Ġact Ġupon </s> Organ ization : Ġorganization Ġinvolved Ġwith Ġevent </s> C urrency : Ġmoney Ġinvolved Ġwith Ġevent </s> The Ġproposed Ġont ology Ġwas Ġdesigned Ġin Ġsuch Ġa Ġmanner Ġthat Ġit Ġcan Ġincorporate Ġinformation Ġextracted Ġfrom Ġmultiple Ġdocuments . ĠIn Ġthis Ġcontext , Ġsuppose Ġthat Ġthe Ġsource Ġof Ġdocuments Ġis Ġa are Ġa Ġlegal Ġpolice Ġdepartment , Ġwhere Ġeach Ġdocument Ġis are Ġunder Ġthe Ġhood Ġof Ġa Ġparticular Ġcase / crime ; Ġfurthermore Further , Ġa Ġsingle Ġcase Ġcan Ġhave Ġdocuments Ġfrom Ġmultiple Ġlanguages . ĠNow , Ġconsidering Ġcase Ġ1 Ġhas Ġ100 Ġdocuments Ġand Ġcase Ġ2 Ġhas Ġ100 Ġdocuments Ġthen Ġthere Ġis Ġnot Ġonly Ġa Ġconnection Ġamong Ġthe Ġdocuments Ġof Ġa Ġsingle Ġcase Ġbut Ġrather Ġamong Ġall Ġthe Ġcases Ġwith Ġall Ġthe Ġcombined Ġ200 Ġdocuments . ĠIn Ġthis Ġway , Ġthe Ġproposed Ġmethod Ġis Ġable Ġto Ġproduce Ġa Ġdetailed Ġand Ġwell - connected Ġknowledge Ġbase . </s> Figure ĠFIG REF 23 Ġshows Ġthe Ġproposed Ġont ology , Ġwhich , Ġin Ġour Ġevaluation Ġprocedure , Ġwas Ġpopulated Ġwith Ġ3 121 Ġevents Ġentries Ġfrom Ġ51 Ġdocuments . </s> P rote ge ĠB IB REF 19 Ġtool Ġwas Ġused Ġfor Ġcreating Ġthe Ġont ology Ġand ĠGraph DB ĠB IB REF 20 Ġfor Ġpop ulating Ġ& Ġquer ying Ġthe Ġdata . ĠGraph DB Ġis Ġan Ġenterprise - ready ĠSem antic ĠGraph ĠDatabase , Ġcompliant Ġwith ĠW 3 C ĠStandards . ĠSem antic ĠGraph ĠDat abases Ġ( also Ġcalled ĠR DF Ġtri pl est ores ) Ġprovide Ġthe Ġcore Ġinfrastructure Ġfor Ġsolutions Ġwhere Ġmodeling Ġagility , Ġdata Ġintegration , Ġrelationship Ġexploration , Ġand Ġcross - enter prise Ġdata Ġpublishing Ġand Ġconsumption Ġare Ġimportant . ĠGraph DB Ġhas Ġa ĠSP AR QL Ġ( SQL - like Ġquery Ġlanguage ) Ġinterface Ġfor ĠR DF Ġgraph Ġdatabases Ġwith Ġthe Ġfollowing Ġtypes : </s> SELECT : Ġreturns Ġtab ular Ġresults </s> CON STRUCT : Ġcreates Ġa Ġnew ĠR DF Ġgraph Ġbased Ġon Ġquery Ġresults </s> AS K : Ġreturns Ġ" YES ", Ġif Ġthe Ġquery Ġhas Ġa Ġsolution , Ġotherwise Ġ" NO " </s> DES C RI BE : Ġreturns ĠR DF Ġdata Ġabout Ġa Ġresource . ĠThis Ġis Ġuseful Ġwhen Ġthe ĠR DF Ġdata Ġstructure Ġin Ġthe Ġdata Ġsource Ġis Ġnot Ġknown </s> INS ERT : Ġinserts Ġtri ples Ġinto Ġa Ġgraph </s> DE LE TE : Ġdelet es Ġtri ples Ġfrom Ġa Ġgraph </s> Furthermore , Ġwe Ġhave Ġextended Ġthe Ġont ology ĠB IB REF 21 Ġto Ġconnect Ġthe Ġextracted Ġterms Ġwith ĠEuro voc Ġcriminal Ġlaw Ġ( disc ussed Ġin Ġsubsection ĠSEC REF 10 ) Ġand ĠI ATE ĠB IB REF 7 Ġterms . ĠI ATE Ġ( Inter active ĠTermin ology Ġfor ĠEurope ) Ġis Ġthe ĠEU 's Ġgeneral Ġterminology Ġdatabase Ġand Ġits Ġaim Ġis Ġto Ġprovide Ġa Ġweb - based Ġinfrastructure Ġfor Ġall ĠEU Ġterminology Ġresources , Ġenhancing Ġthe Ġavailability Ġand Ġstandard ization Ġof Ġthe Ġinformation . ĠThe Ġextended Ġont ology Ġhas Ġa Ġnumber Ġof Ġsub - classes Ġfor ĠActor , ĠEvent , ĠObject Ġand ĠPlace Ġclasses Ġdetailed Ġin ĠTable ĠT AB REF 30 . </s> Discussion </s> We Ġhave Ġdefined Ġa Ġmajor Ġdesign Ġprinciple Ġfor Ġour Ġarchitecture : Ġit Ġshould Ġbe Ġmodular Ġand Ġnot Ġrely Ġon Ġhuman Ġmade Ġrules Ġallowing , Ġas Ġmuch Ġas Ġpossible , Ġits Ġindependence Ġfrom Ġa Ġspecific Ġlanguage . ĠIn Ġthis Ġway , Ġits Ġpotential Ġapplication Ġto Ġanother Ġlanguage Ġwould Ġbe Ġeasier , Ġsimply Ġby Ġchanging Ġthe Ġmodules Ġor Ġthe Ġmodels Ġof Ġspecific Ġmodules . ĠIn Ġfact , Ġwe Ġhave Ġexplored Ġthe Ġuse Ġof Ġalready Ġexisting Ġmodules Ġand Ġadopted Ġand Ġintegrated Ġseveral Ġof Ġthese Ġtools Ġinto Ġour Ġpipeline . </s> It Ġis Ġimportant Ġto Ġpoint Ġout Ġthat , Ġas Ġfar Ġas Ġwe Ġknow , Ġthere Ġis Ġno Ġintegrated Ġarchitecture Ġsupporting Ġthe Ġfull Ġprocessing Ġpipeline Ġfor Ġthe ĠPortuguese Ġlanguage . ĠWe Ġevaluated Ġseveral Ġsystems Ġlike ĠRem brand t ĠB IB REF 22 Ġor ĠLing ua Kit : Ġthe Ġformer Ġonly Ġhas Ġthe Ġinitial Ġsteps Ġof Ġour Ġproposal Ġ( until ĠN ER ) Ġand Ġthe Ġlater Ġperformed Ġworse Ġthan Ġour Ġsystem . </s> This Ġframework , Ġdeveloped Ġwithin Ġthe Ġcontext Ġof Ġthe ĠAg atha Ġproject Ġ( described Ġin ĠSection ĠSEC REF 1 ) Ġhas Ġthe Ġfull Ġprocessing Ġpipeline Ġfor ĠPortuguese Ġtexts : Ġit Ġreceives Ġsentences Ġas Ġinput Ġand Ġoutputs Ġont ological Ġinformation : Ġa ) Ġfirst Ġperforms Ġall ĠN LP Ġtypical Ġtasks Ġuntil Ġsemantic Ġrole Ġlab elling ; Ġb ) Ġthen , Ġit Ġextracts Ġsubject - verb - object Ġtri ples ; Ġc ) Ġand , Ġthen , Ġit Ġperforms Ġont ology Ġmatching Ġprocedures . ĠAs Ġa Ġfinal Ġresult , Ġthe Ġobtained Ġoutput Ġis Ġinserted Ġinto Ġa Ġspecialized Ġont ology . </s> We Ġare Ġaware Ġthat Ġeach Ġof Ġthe Ġarchitecture Ġmodules Ġcan , Ġand Ġshould , Ġbe Ġimproved Ġbut Ġour Ġmain Ġgoal Ġwas Ġthe Ġcreation Ġof Ġa Ġfull Ġworking Ġtext Ġprocessing Ġpipeline Ġfor Ġthe ĠPortuguese Ġlanguage . </s> Con clusions Ġand ĠFuture ĠWork </s> Besides Ġthe Ġend âĢĵ to âĢĵ end ĠN LP Ġpipeline Ġfor Ġthe ĠPortuguese Ġlanguage , Ġthe Ġother Ġmain Ġcontributions Ġof Ġthis Ġwork Ġcan Ġbe Ġsummarize Ġas Ġfollows : </s> Development Ġof Ġan Ġont ology Ġfor Ġthe Ġcriminal Ġlaw Ġdomain ; </s> Al ignment Ġof Ġthe ĠEuro voc Ġthe s aurus Ġand ĠI ATE Ġterminology Ġwith Ġthe Ġont ology Ġcreated ; </s> Represent ation Ġof Ġthe Ġextracted Ġevents Ġfrom Ġtexts Ġin Ġthe Ġlinked Ġknowledge Ġbase Ġdefined . </s> The Ġobtained Ġresults Ġsupport Ġour Ġclaim Ġthat Ġthe Ġproposed Ġsystem Ġcan Ġbe Ġused Ġas Ġa Ġbase Ġtool Ġfor Ġinformation Ġextraction Ġfor Ġthe ĠPortuguese Ġlanguage . ĠBeing Ġcomposed Ġby Ġseveral Ġmodules , Ġeach Ġof Ġthem Ġwith Ġa Ġhigh Ġlevel Ġof Ġcomplexity , Ġit Ġis Ġcertain Ġthat Ġour Ġapproach Ġcan Ġbe Ġimproved Ġand Ġan Ġoverall Ġbetter Ġperformance Ġcan Ġbe Ġachieved . </s> As Ġfuture Ġwork Ġwe Ġintend , Ġnot Ġonly Ġto Ġcontinue Ġimproving Ġthe Ġindividual Ġmodules , Ġbut Ġalso Ġplan Ġto Ġextend Ġthis Ġwork Ġto Ġthe : </s> automatic Ġcreation Ġof Ġevent Ġtimelines ; </s> inc orpor ation Ġin Ġthe Ġknowledge Ġbase Ġof Ġinformation Ġobtained Ġfrom Ġvideos Ġor Ġpictures Ġdescribing Ġscenes Ġrelevant Ġto Ġcriminal Ġinvestigations . </s> Acknowled gments </s> The Ġauthors Ġwould Ġlike Ġto Ġthank ĠCOMP ET E Ġ2020 , ĠP ORT UG AL Ġ2020 ĠProgram , Ġthe ĠEuropean ĠUnion , Ġand ĠAL ENT E JO Ġ2020 Ġfor Ġsupporting Ġthis Ġresearch Ġas Ġpart Ġof ĠAg atha ĠProject ĠSI Ġ& ĠID T Ġnumber Ġ180 22 Ġ( Int elligent Ġanalysis Ġsystem Ġof Ġopen Ġof Ġsources Ġinformation Ġfor Ġsurveillance / crime Ġcontrol ). ĠThe Ġauthors Ġwould Ġalso Ġlike Ġto Ġthank ĠL IS P Ġ- ĠLaboratory Ġof ĠIn format ics , ĠSystems Ġand ĠParallel ism .