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1908.09246
Introduction </s> With Ġthe Ġincreasing Ġpopularity Ġof Ġthe ĠInternet , Ġonline Ġtexts Ġprovided Ġby Ġsocial Ġmedia Ġplatform Ġ( e . g . ĠTwitter ) Ġand Ġnews Ġmedia Ġsites Ġ( e . g . ĠGoogle Ġnews ) Ġhave Ġbecome Ġimportant Ġsources Ġof Ġreal - world Ġevents . ĠTherefore , Ġit Ġis Ġcrucial Ġto Ġautomatically Ġextract Ġevents Ġfrom Ġonline Ġtexts . </s> Due Ġto Ġthe Ġhigh Ġvariety Ġof Ġevents Ġdiscussed Ġonline Ġand Ġthe Ġdifficulty Ġin Ġobtaining Ġannot ated Ġdata Ġfor Ġtraining , Ġtraditional Ġtemplate - based Ġor Ġsupervised Ġlearning Ġapproaches Ġfor Ġevent Ġextraction Ġare Ġno Ġlonger Ġapplicable Ġin Ġdealing Ġwith Ġonline Ġtexts . ĠNevertheless , Ġnews worthy Ġevents Ġare Ġoften Ġdiscussed Ġby Ġmany Ġtweets Ġor Ġonline Ġnews Ġarticles . ĠTherefore , Ġthe Ġsame Ġevent Ġcould Ġbe Ġmentioned Ġby Ġa Ġhigh Ġvolume Ġof Ġredundant Ġtweets Ġor Ġnews Ġarticles . ĠThis Ġproperty Ġinspires Ġthe Ġresearch Ġcommunity Ġto Ġdevise Ġclust ering - based Ġmodels ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġto Ġdiscover Ġnew Ġor Ġpreviously Ġunidentified Ġevents Ġwithout Ġextracting Ġstructured Ġrepresentations . </s> To Ġextract Ġstructured Ġrepresentations Ġof Ġevents Ġsuch Ġas Ġwho Ġdid Ġwhat , Ġwhen , Ġwhere Ġand Ġwhy , ĠBay esian Ġapproaches Ġhave Ġmade Ġsome Ġprogress . ĠAssuming Ġthat Ġeach Ġdocument Ġis Ġassigned Ġto Ġa Ġsingle Ġevent , Ġwhich Ġis Ġmodeled Ġas Ġa Ġjoint Ġdistribution Ġover Ġthe Ġnamed Ġentities , Ġthe Ġdate Ġand Ġthe Ġlocation Ġof Ġthe Ġevent , Ġand Ġthe Ġevent - related Ġkeywords , ĠZhou Ġet Ġal . Ġz hou 2014 simple Ġproposed Ġan Ġun super vised ĠLat ent ĠEvent ĠModel Ġ( LE M ) Ġfor Ġopen - domain Ġevent Ġextraction . ĠTo Ġaddress Ġthe Ġlimitation Ġthat ĠL EM Ġrequires Ġthe Ġnumber Ġof Ġevents Ġto Ġbe Ġpre - set , ĠZhou Ġet Ġal . Ġz hou 2017 event Ġfurther Ġproposed Ġthe ĠDir ich let ĠProcess ĠEvent ĠM ixture ĠModel Ġ( DP EM M ) Ġin Ġwhich Ġthe Ġnumber Ġof Ġevents Ġcan Ġbe Ġlearned Ġautomatically Ġfrom Ġdata . ĠHowever , Ġboth ĠL EM Ġand ĠDP EM M Ġhave Ġtwo Ġlimitations : Ġ( 1 ) Ġthey Ġassume Ġthat Ġall Ġwords Ġin Ġa Ġdocument Ġare Ġgenerated Ġfrom Ġa Ġsingle Ġevent Ġwhich Ġcan Ġbe Ġrepresented Ġby Ġa Ġquadru ple ĠIN LINE FORM 0 Ġentity , Ġlocation , Ġkeyword , Ġdate ĠIN LINE FORM 1 Ġ. ĠHowever , Ġlong Ġtexts Ġsuch Ġnews Ġarticles Ġoften Ġdescribe Ġmultiple Ġevents Ġwhich Ġclearly Ġviolates Ġthis Ġassumption ; Ġ( 2 ) ĠDuring Ġthe Ġinference Ġprocess Ġof Ġboth Ġapproaches , Ġthe ĠGibbs Ġsam pler Ġneeds Ġto Ġcompute Ġthe Ġconditional Ġposterior Ġdistribution Ġand Ġassigns Ġan Ġevent Ġfor Ġeach Ġdocument . ĠThis Ġis Ġtime Ġconsuming Ġand Ġtakes Ġlong Ġtime Ġto Ġconverge . </s> To Ġdeal Ġwith Ġthese Ġlimitations , Ġin Ġthis Ġpaper , Ġwe Ġpropose Ġthe ĠAd vers arial - ne ural ĠEvent ĠModel Ġ( A EM ) Ġbased Ġon Ġadvers arial Ġtraining Ġfor Ġopen - domain Ġevent Ġextraction . ĠThe Ġprinciple Ġidea Ġis Ġto Ġuse Ġa Ġgenerator Ġnetwork Ġto Ġlearn Ġthe Ġprojection Ġfunction Ġbetween Ġthe Ġdocument - event Ġdistribution Ġand Ġfour Ġevent Ġrelated Ġword Ġdistributions Ġ( entity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution ). ĠInstead Ġof Ġproviding Ġan Ġanalytic Ġapproximation , ĠA EM Ġuses Ġa Ġdiscrim inator Ġnetwork Ġto Ġdiscriminate Ġbetween Ġthe Ġreconstructed Ġdocuments Ġfrom Ġlatent Ġevents Ġand Ġthe Ġoriginal Ġinput Ġdocuments . ĠThis Ġessentially Ġhelps Ġthe Ġgenerator Ġto Ġconstruct Ġa Ġmore Ġrealistic Ġdocument Ġfrom Ġa Ġrandom Ġnoise Ġdrawn Ġfrom Ġa ĠDir ich let Ġdistribution . ĠDue Ġto Ġthe Ġflexibility Ġof Ġneural Ġnetworks , Ġthe Ġgenerator Ġis Ġcapable Ġof Ġlearning Ġcomplicated Ġnon linear Ġdistributions . ĠAnd Ġthe Ġsupervision Ġsignal Ġprovided Ġby Ġthe Ġdiscrim inator Ġwill Ġhelp Ġgenerator Ġto Ġcapture Ġthe Ġevent - related Ġpatterns . ĠFurthermore , Ġthe Ġdiscrim inator Ġalso Ġprovides Ġlow - dimensional Ġdiscrim in ative Ġfeatures Ġwhich Ġcan Ġbe Ġused Ġto Ġvisualize Ġdocuments Ġand Ġevents . </s> The Ġmain Ġcontributions Ġof Ġthe Ġpaper Ġare Ġsummarized Ġbelow : </s> Related ĠWork </s> Our Ġwork Ġis Ġrelated Ġto Ġtwo Ġlines Ġof Ġresearch , Ġevent Ġextraction Ġand ĠGener ative ĠAd vers arial ĠNets . </s> Event ĠExt raction </s> Recently Ġthere Ġhas Ġbeen Ġmuch Ġinterest Ġin Ġevent Ġextraction Ġfrom Ġonline Ġtexts , Ġand Ġapproaches Ġcould Ġbe Ġcategorized Ġas Ġdomain - specific Ġand Ġopen - domain Ġevent Ġextraction . </s> Domain - specific Ġevent Ġextraction Ġoften Ġfocuses Ġon Ġthe Ġspecific Ġtypes Ġof Ġevents Ġ( e . g . Ġsports Ġevents Ġor Ġcity Ġevents ). ĠPan em Ġet Ġal . Ġpan em 2014 struct ured Ġdevised Ġa Ġnovel Ġalgorithm Ġto Ġextract Ġattribute - value Ġpairs Ġand Ġmapped Ġthem Ġto Ġmanually Ġgenerated Ġschemes Ġfor Ġextracting Ġthe Ġnatural Ġdisaster Ġevents . ĠSimilarly , Ġto Ġextract Ġthe Ġcity - tra ffic Ġrelated Ġevent , ĠAn anth aram Ġet Ġal . Ġan anth aram 2015 ext ract ing Ġviewed Ġthe Ġtask Ġas Ġa Ġsequential Ġtagging Ġproblem Ġand Ġproposed Ġan Ġapproach Ġbased Ġon Ġthe Ġconditional Ġrandom Ġfields . ĠZhang Ġz hang 2018 event Ġproposed Ġan Ġevent Ġextraction Ġapproach Ġbased Ġon Ġimitation Ġlearning , Ġespecially Ġon Ġinverse Ġreinforcement Ġlearning . </s> Open - domain Ġevent Ġextraction Ġaims Ġto Ġextract Ġevents Ġwithout Ġlimiting Ġthe Ġspecific Ġtypes Ġof Ġevents . ĠTo Ġanalyze Ġindividual Ġmessages Ġand Ġinduce Ġa Ġcanonical Ġvalue Ġfor Ġeach Ġevent , ĠBenson Ġet Ġal . Ġb enson 2011 event Ġproposed Ġan Ġapproach Ġbased Ġon Ġa Ġstructured Ġgraphical Ġmodel . ĠBy Ġrepresenting Ġan Ġevent Ġwith Ġa Ġbinary Ġtuple Ġwhich Ġis Ġconstituted Ġby Ġa Ġnamed Ġentity Ġand Ġa Ġdate , ĠR itter Ġet Ġal . Ġr itter 2012 open Ġemployed Ġsome Ġstatistic Ġto Ġmeasure Ġthe Ġstrength Ġof Ġassociations Ġbetween Ġa Ġnamed Ġentity Ġand Ġa Ġdate . ĠThe Ġproposed Ġsystem Ġrelies Ġon Ġa Ġsupervised Ġlabel er Ġtrained Ġon Ġannot ated Ġdata . ĠIn ĠB IB REF 1 Ġ, ĠAbdel ha q Ġet Ġal . Ġdeveloped Ġa Ġreal - time Ġevent Ġextraction Ġsystem Ġcalled ĠEven Tweet , Ġand Ġeach Ġevent Ġis Ġrepresented Ġas Ġa Ġtriple Ġconstituted Ġby Ġtime , Ġlocation Ġand Ġkeywords . ĠTo Ġextract Ġmore Ġinformation , ĠWang Ġel Ġal . Ġw ang 2015 see ft Ġdeveloped Ġa Ġsystem Ġemploying Ġthe Ġlinks Ġin Ġtweets Ġand Ġcomb ing Ġtweets Ġwith Ġlinked Ġarticles Ġto Ġidentify Ġevents . ĠXia Ġel Ġal . Ġx ia 2015 new Ġcombined Ġtexts Ġwith Ġthe Ġlocation Ġinformation Ġto Ġdetect Ġthe Ġevents Ġwith Ġlow Ġspatial Ġand Ġtemporal Ġdeviations . ĠZhou Ġet Ġal . Ġz hou 2014 simple , zhou 2017 event Ġrepresented Ġevent Ġas Ġa Ġquadru ple Ġand Ġproposed Ġtwo ĠBay esian Ġmodels Ġto Ġextract Ġevents Ġfrom Ġtweets . </s> Gener ative ĠAd vers arial ĠNets </s> As Ġa Ġneural - based Ġgener ative Ġmodel , ĠGener ative ĠAd vers arial ĠNets ĠB IB REF 3 Ġhave Ġbeen Ġextensively Ġresearched Ġin Ġnatural Ġlanguage Ġprocessing Ġ( N LP ) Ġcommunity . </s> For Ġtext Ġgeneration , Ġthe Ġsequence Ġgener ative Ġadvers arial Ġnetwork Ġ( Se q GAN ) Ġproposed Ġin ĠB IB REF 4 Ġincorporated Ġa Ġpolicy Ġgradient Ġstrategy Ġto Ġoptimize Ġthe Ġgeneration Ġprocess . ĠBased Ġon Ġthe Ġpolicy Ġgradient , ĠLin Ġet Ġal . Ġlin 2017 ad vers arial Ġproposed ĠRank GAN Ġto Ġcapture Ġthe Ġrich Ġstructures Ġof Ġlanguage Ġby Ġranking Ġand Ġanalyzing Ġa Ġcollection Ġof Ġhuman - written Ġand Ġmachine - written Ġsentences . ĠTo Ġovercome Ġmode Ġcollapse Ġwhen Ġdealing Ġwith Ġdiscrete Ġdata , ĠFed us Ġet Ġal . Ġfed us 2018 mask gan Ġproposed ĠMask GAN Ġwhich Ġused Ġan Ġactor - crit ic Ġconditional ĠG AN Ġto Ġfill Ġin Ġmissing Ġtext Ġconditioned Ġon Ġthe Ġsurrounding Ġcontext . ĠAlong Ġthis Ġline , ĠWang Ġet Ġal . Ġw ang 2018 sent igan Ġproposed ĠSent i GAN Ġto Ġgenerate Ġtexts Ġof Ġdifferent Ġsentiment Ġlabels . ĠBesides , ĠLi Ġet Ġal . Ġli 2018 learning Ġimproved Ġthe Ġperformance Ġof Ġsemi - super vised Ġtext Ġclassification Ġusing Ġadvers arial Ġtraining , ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġdesigned ĠG AN - based Ġmodels Ġfor Ġdistance Ġsupervision Ġrelation Ġextraction . </s> Although Ġvarious ĠG AN Ġbased Ġapproaches Ġhave Ġbeen Ġexplored Ġfor Ġmany Ġapplications , Ġnone Ġof Ġthese Ġapproaches Ġtackles Ġopen - domain Ġevent Ġextraction Ġfrom Ġonline Ġtexts . ĠWe Ġpropose Ġa Ġnovel ĠG AN - based Ġevent Ġextraction Ġmodel Ġcalled ĠA EM . ĠCompared Ġwith Ġthe Ġprevious Ġmodels , ĠA EM Ġhas Ġthe Ġfollowing Ġdifferences : Ġ( 1 ) ĠUnlike Ġmost ĠG AN - based Ġtext Ġgeneration Ġapproaches , Ġa Ġgenerator Ġnetwork Ġis Ġemployed Ġin ĠA EM Ġto Ġlearn Ġthe Ġprojection Ġfunction Ġbetween Ġan Ġevent Ġdistribution Ġand Ġthe Ġevent - related Ġword Ġdistributions Ġ( entity , Ġlocation , Ġkeyword , Ġdate ). ĠThe Ġlearned Ġgenerator Ġcaptures Ġevent - related Ġpatterns Ġrather Ġthan Ġgenerating Ġtext Ġsequence ; Ġ( 2 ) ĠDifferent Ġfrom ĠL EM Ġand ĠDP EM M , ĠA EM Ġuses Ġa Ġgenerator Ġnetwork Ġto Ġcapture Ġthe Ġevent - related Ġpatterns Ġand Ġis Ġable Ġto Ġmine Ġevents Ġfrom Ġdifferent Ġtext Ġsources Ġ( short Ġand Ġlong ). ĠMoreover , Ġunlike Ġtraditional Ġinference Ġprocedure , Ġsuch Ġas ĠGibbs Ġsampling Ġused Ġin ĠL EM Ġand ĠDP EM M , ĠA EM Ġcould Ġextract Ġthe Ġevents Ġmore Ġefficiently Ġdue Ġto Ġthe ĠCU DA Ġacceleration ; Ġ( 3 ) ĠThe Ġdiscrim in ative Ġfeatures Ġlearned Ġby Ġthe Ġdiscrim inator Ġof ĠA EM Ġprovide Ġa Ġstraightforward Ġway Ġto Ġvisualize Ġthe Ġextracted Ġevents . </s> Method ology </s> We Ġdescribe ĠAd vers arial - ne ural ĠEvent ĠModel Ġ( A EM ) Ġin Ġthis Ġsection . ĠAn Ġevent Ġis Ġrepresented Ġas Ġa Ġquadru ple Ġ< ĠIN LINE FORM 0 Ġ> , Ġwhere ĠIN LINE FORM 1 Ġstands Ġfor Ġnon - location Ġnamed Ġentities , ĠIN LINE FORM 2 Ġfor Ġa Ġlocation , ĠIN LINE FORM 3 Ġfor Ġevent - related Ġkeywords , ĠIN LINE FORM 4 Ġfor Ġa Ġdate , Ġand Ġeach Ġcomponent Ġin Ġthe Ġtuple Ġis Ġrepresented Ġby Ġcomponent - specific Ġrepresentative Ġwords . </s> A EM Ġis Ġconstituted Ġby Ġthree Ġcomponents : Ġ( 1 ) ĠThe Ġdocument Ġrepresentation Ġmodule , Ġas Ġshown Ġat Ġthe Ġtop Ġof ĠFigure ĠFIG REF 4 Ġ, Ġdefines Ġa Ġdocument Ġrepresentation Ġapproach Ġwhich Ġconverts Ġan Ġinput Ġdocument Ġfrom Ġthe Ġonline Ġtext Ġcorpus Ġinto ĠIN LINE FORM 0 Ġwhich Ġcaptures Ġthe Ġkey Ġevent Ġelements ; Ġ( 2 ) ĠThe Ġgenerator ĠIN LINE FORM 1 Ġ, Ġas Ġshown Ġin Ġthe Ġlower - left Ġpart Ġof ĠFigure ĠFIG REF 4 Ġ, Ġgenerates Ġa Ġfake Ġdocument ĠIN LINE FORM 2 Ġwhich Ġis Ġconstituted Ġby Ġfour Ġmult in omial Ġdistributions Ġusing Ġan Ġevent Ġdistribution ĠIN LINE FORM 3 Ġdrawn Ġfrom Ġa ĠDir ich let Ġdistribution Ġas Ġinput ; Ġ( 3 ) ĠThe Ġdiscrim inator ĠIN LINE FORM 4 Ġ, Ġas Ġshown Ġin Ġthe Ġlower - right Ġpart Ġof ĠFigure ĠFIG REF 4 Ġ, Ġdistinguishes Ġthe Ġreal Ġdocuments Ġfrom Ġthe Ġfake Ġones Ġand Ġits Ġoutput Ġis Ġsubsequently Ġemployed Ġas Ġa Ġlearning Ġsignal Ġto Ġupdate Ġthe ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġ. ĠThe Ġdetails Ġof Ġeach Ġcomponent Ġare Ġpresented Ġbelow . </s> Document ĠRepresent ation </s> Each Ġdocument ĠIN LINE FORM 0 Ġin Ġa Ġgiven Ġcorpus ĠIN LINE FORM 1 Ġis Ġrepresented Ġas Ġa Ġconc aten ation Ġof Ġ4 Ġmult in omial Ġdistributions Ġwhich Ġare Ġentity Ġdistribution Ġ( ĠIN LINE FORM 2 Ġ), Ġlocation Ġdistribution Ġ( ĠIN LINE FORM 3 Ġ), Ġkeyword Ġdistribution Ġ( ĠIN LINE FORM 4 Ġ) Ġand Ġdate Ġdistribution Ġ( ĠIN LINE FORM 5 Ġ) Ġof Ġthe Ġdocument . ĠAs Ġfour Ġdistributions Ġare Ġcalculated Ġin Ġa Ġsimilar Ġway , Ġwe Ġonly Ġdescribe Ġthe Ġcomputation Ġof Ġthe Ġentity Ġdistribution Ġbelow Ġas Ġan Ġexample . </s> The Ġentity Ġdistribution ĠIN LINE FORM 0 Ġis Ġrepresented Ġby Ġa Ġnormalized ĠIN LINE FORM 1 Ġ- dimensional Ġvector Ġweighted Ġby ĠTF - ID F , Ġand Ġit Ġis Ġcalculated Ġas : ĠIN LINE FORM 2 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġpseudo Ġcorpus Ġconstructed Ġby Ġremoving Ġall Ġnon - entity Ġwords Ġfrom ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġis Ġthe Ġtotal Ġnumber Ġof Ġdistinct Ġentities Ġin Ġa Ġcorpus , ĠIN LINE FORM 3 Ġdenotes Ġthe Ġnumber Ġof ĠIN LINE FORM 4 Ġ- th Ġentity Ġappeared Ġin Ġdocument ĠIN LINE FORM 5 Ġ, ĠIN LINE FORM 6 Ġrepresents Ġthe Ġnumber Ġof Ġdocuments Ġin Ġthe Ġcorpus , Ġand ĠIN LINE FORM 7 Ġis Ġthe Ġnumber Ġof Ġdocuments Ġthat Ġcontain ĠIN LINE FORM 8 Ġ- th Ġentity , Ġand Ġthe Ġobtained ĠIN LINE FORM 9 Ġdenotes Ġthe Ġrelevance Ġbetween ĠIN LINE FORM 10 Ġ- th Ġentity Ġand Ġdocument ĠIN LINE FORM 11 Ġ. </s> Similarly , Ġlocation Ġdistribution ĠIN LINE FORM 0 Ġ, Ġkeyword Ġdistribution ĠIN LINE FORM 1 Ġand Ġdate Ġdistribution ĠIN LINE FORM 2 Ġof ĠIN LINE FORM 3 Ġcould Ġbe Ġcalculated Ġin Ġthe Ġsame Ġway , Ġand Ġthe Ġdimensions Ġof Ġthese Ġdistributions Ġare Ġden oted Ġas ĠIN LINE FORM 4 Ġ, ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġ, Ġrespectively . ĠFinally , Ġeach Ġdocument ĠIN LINE FORM 7 Ġin Ġthe Ġcorpus Ġis Ġrepresented Ġby Ġa ĠIN LINE FORM 8 Ġ- dimensional Ġ( ĠIN LINE FORM 9 Ġ= ĠIN LINE FORM 10 Ġ+ ĠIN LINE FORM 11 Ġ+ ĠIN LINE FORM 12 Ġ+ ĠIN LINE FORM 13 Ġ) Ġvector ĠIN LINE FORM 14 Ġby Ġconc aten ating Ġfour Ġcomputed Ġdistributions . </s> Network ĠArchitecture </s> The Ġgenerator Ġnetwork ĠIN LINE FORM 0 Ġis Ġdesigned Ġto Ġlearn Ġthe Ġprojection Ġfunction Ġbetween Ġthe Ġdocument - event Ġdistribution ĠIN LINE FORM 1 Ġand Ġthe Ġfour Ġdocument - level Ġword Ġdistributions Ġ( entity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution ). </s> More Ġconcrete ly , ĠIN LINE FORM 0 Ġconsists Ġof Ġa ĠIN LINE FORM 1 Ġ- dimensional Ġdocument - event Ġdistribution Ġlayer , ĠIN LINE FORM 2 Ġ- dimensional Ġhidden Ġlayer Ġand ĠIN LINE FORM 3 Ġ- dimensional Ġevent - related Ġword Ġdistribution Ġlayer . ĠHere , ĠIN LINE FORM 4 Ġdenotes Ġthe Ġevent Ġnumber , ĠIN LINE FORM 5 Ġis Ġthe Ġnumber Ġof Ġunits Ġin Ġthe Ġhidden Ġlayer , ĠIN LINE FORM 6 Ġis Ġthe Ġvocabulary Ġsize Ġand Ġequals Ġto ĠIN LINE FORM 7 Ġ+ ĠIN LINE FORM 8 Ġ+ ĠIN LINE FORM 9 Ġ+ ĠIN LINE FORM 10 Ġ. ĠAs Ġshown Ġin ĠFigure ĠFIG REF 4 Ġ, ĠIN LINE FORM 11 Ġfirst ly Ġemploys Ġa Ġrandom Ġdocument - event Ġdistribution ĠIN LINE FORM 12 Ġas Ġan Ġinput . ĠTo Ġmodel Ġthe Ġmult in omial Ġproperty Ġof Ġthe Ġdocument - event Ġdistribution , ĠIN LINE FORM 13 Ġis Ġdrawn Ġfrom Ġa ĠDir ich let Ġdistribution Ġparameter ized Ġwith ĠIN LINE FORM 14 Ġwhich Ġis Ġformulated Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġhyper - param eter Ġof Ġthe Ġdir ich let Ġdistribution , ĠIN LINE FORM 1 Ġis Ġthe Ġnumber Ġof Ġevents Ġwhich Ġshould Ġbe Ġset Ġin ĠA EM , ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġrepresents Ġthe Ġproportion Ġof Ġevent ĠIN LINE FORM 4 Ġin Ġthe Ġdocument Ġand ĠIN LINE FORM 5 Ġ. </s> Sub sequently , ĠIN LINE FORM 0 Ġtransforms ĠIN LINE FORM 1 Ġinto Ġa ĠIN LINE FORM 2 Ġ- dimensional Ġhidden Ġspace Ġusing Ġa Ġlinear Ġlayer Ġfollowed Ġby Ġlayer Ġnormal ization , Ġand Ġthe Ġtransformation Ġis Ġdefined Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġrepresents Ġthe Ġweight Ġmatrix Ġof Ġhidden Ġlayer , Ġand ĠIN LINE FORM 1 Ġdenotes Ġthe Ġbias Ġterm , ĠIN LINE FORM 2 Ġis Ġthe Ġparameter Ġof ĠLe aky Re LU Ġactivation Ġand Ġis Ġset Ġto Ġ0 . 1 , ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġdenote Ġthe Ġnormalized Ġhidden Ġstates Ġand Ġthe Ġoutputs Ġof Ġthe Ġhidden Ġlayer , Ġand ĠIN LINE FORM 5 Ġrepresents Ġthe Ġlayer Ġnormal ization . </s> Then , Ġto Ġproject ĠIN LINE FORM 0 Ġinto Ġfour Ġdocument - level Ġevent Ġrelated Ġword Ġdistributions Ġ( ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġshown Ġin ĠFigure ĠFIG REF 4 Ġ), Ġfour Ġsub nets Ġ( each Ġcontains Ġa Ġlinear Ġlayer , Ġa Ġbatch Ġnormal ization Ġlayer Ġand Ġa Ġsoft max Ġlayer ) Ġare Ġemployed Ġin ĠIN LINE FORM 5 Ġ. ĠAnd Ġthe Ġexact Ġtransformation Ġis Ġbased Ġon Ġthe Ġformulas Ġbelow : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġmeans Ġsoft max Ġlayer , ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġdenote Ġthe Ġweight Ġmat rices Ġof Ġthe Ġlinear Ġlayers Ġin Ġsub nets , ĠIN LINE FORM 5 Ġ, ĠIN LINE FORM 6 Ġ, ĠIN LINE FORM 7 Ġand ĠIN LINE FORM 8 Ġrepresent Ġthe Ġcorresponding Ġbias Ġterms , ĠIN LINE FORM 9 Ġ, ĠIN LINE FORM 10 Ġ, ĠIN LINE FORM 11 Ġand ĠIN LINE FORM 12 Ġare Ġstate Ġvectors . ĠIN LINE FORM 13 Ġ, ĠIN LINE FORM 14 Ġ, ĠIN LINE FORM 15 Ġand ĠIN LINE FORM 16 Ġdenote Ġthe Ġgenerated Ġentity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution , Ġrespectively , Ġthat Ġcorrespond Ġto Ġthe Ġgiven Ġevent Ġdistribution ĠIN LINE FORM 17 Ġ. ĠAnd Ġeach Ġdimension Ġrepresents Ġthe Ġrelevance Ġbetween Ġcorresponding Ġentity / location / key word / date Ġterm Ġand Ġthe Ġinput Ġevent Ġdistribution . </s> Finally , Ġfour Ġgenerated Ġdistributions Ġare Ġconc aten ated Ġto Ġrepresent Ġthe Ġgenerated Ġdocument ĠIN LINE FORM 0 Ġcorresponding Ġto Ġthe Ġinput ĠIN LINE FORM 1 Ġ: ĠDIS PLAY FORM 0 </s> The Ġdiscrim inator Ġnetwork ĠIN LINE FORM 0 Ġis Ġdesigned Ġas Ġa Ġfully - connected Ġnetwork Ġwhich Ġcontains Ġan Ġinput Ġlayer , Ġa Ġdiscrim in ative Ġfeature Ġlayer Ġ( disc rim in ative Ġfeatures Ġare Ġemployed Ġfor Ġevent Ġvisualization ) Ġand Ġan Ġoutput Ġlayer . ĠIn ĠA EM , ĠIN LINE FORM 1 Ġuses Ġfake Ġdocument ĠIN LINE FORM 2 Ġand Ġreal Ġdocument ĠIN LINE FORM 3 Ġas Ġinput Ġand Ġoutputs Ġthe Ġsignal ĠIN LINE FORM 4 Ġto Ġindicate Ġthe Ġsource Ġof Ġthe Ġinput Ġdata Ġ( lower Ġvalue Ġdenotes Ġthat ĠIN LINE FORM 5 Ġis Ġprone Ġto Ġpredict Ġthe Ġinput Ġdata Ġas Ġa Ġfake Ġdocument Ġand Ġvice Ġversa ). </s> As Ġhave Ġpreviously Ġbeen Ġdiscussed Ġin ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, Ġlips ch itz Ġcontinuity Ġof ĠIN LINE FORM 0 Ġnetwork Ġis Ġcrucial Ġto Ġthe Ġtraining Ġof Ġthe ĠG AN - based Ġapproaches . ĠTo Ġensure Ġthe Ġlips ch itz Ġcontinuity Ġof ĠIN LINE FORM 1 Ġ, Ġwe Ġemploy Ġthe Ġspectral Ġnormal ization Ġtechnique ĠB IB REF 9 Ġ. ĠMore Ġconcrete ly , Ġfor Ġeach Ġlinear Ġlayer ĠIN LINE FORM 2 Ġ( b ias Ġterm Ġis Ġomitted Ġfor Ġsimplicity ) Ġin ĠIN LINE FORM 3 Ġ, Ġthe Ġweight Ġmatrix ĠIN LINE FORM 4 Ġis Ġnormalized Ġby ĠIN LINE FORM 5 Ġ. ĠHere , ĠIN LINE FORM 6 Ġis Ġthe Ġspectral Ġnorm Ġof Ġthe Ġweight Ġmatrix ĠIN LINE FORM 7 Ġwith Ġthe Ġdefinition Ġbelow : ĠDIS PLAY FORM 0 </s> which Ġis Ġequivalent Ġto Ġthe Ġlargest Ġsingular Ġvalue Ġof ĠIN LINE FORM 0 Ġ. ĠThe Ġweight Ġmatrix ĠIN LINE FORM 1 Ġis Ġthen Ġnormalized Ġusing : ĠDIS PLAY FORM 0 </s> Obviously , Ġthe Ġnormalized Ġweight Ġmatrix ĠIN LINE FORM 0 Ġsatisfies Ġthat ĠIN LINE FORM 1 Ġand Ġfurther Ġensures Ġthe Ġlips ch itz Ġcontinuity Ġof Ġthe ĠIN LINE FORM 2 Ġnetwork ĠB IB REF 9 Ġ. ĠTo Ġreduce Ġthe Ġhigh Ġcost Ġof Ġcomputing Ġspectral Ġnorm ĠIN LINE FORM 3 Ġusing Ġsingular Ġvalue Ġdecom position Ġat Ġeach Ġiteration , Ġwe Ġfollow ĠB IB REF 10 Ġand Ġemploy Ġthe Ġpower Ġiteration Ġmethod Ġto Ġestimate ĠIN LINE FORM 4 Ġinstead . ĠWith Ġthis Ġsubstitution , Ġthe Ġspectral Ġnorm Ġcan Ġbe Ġestimated Ġwith Ġvery Ġsmall Ġadditional Ġcomputational Ġtime . </s> Object ive Ġand ĠTraining ĠProcedure </s> The Ġreal Ġdocument ĠIN LINE FORM 0 Ġand Ġfake Ġdocument ĠIN LINE FORM 1 Ġshown Ġin ĠFigure ĠFIG REF 4 Ġcould Ġbe Ġviewed Ġas Ġrandom Ġsamples Ġfrom Ġtwo Ġdistributions ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġ, Ġand Ġeach Ġof Ġthem Ġis Ġa Ġjoint Ġdistribution Ġconstituted Ġby Ġfour ĠDir ich let Ġdistributions Ġ( cor respond ing Ġto Ġentity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution ). ĠThe Ġtraining Ġobjective Ġof ĠA EM Ġis Ġto Ġlet Ġthe Ġdistribution ĠIN LINE FORM 4 Ġ( produced Ġby ĠIN LINE FORM 5 Ġnetwork ) Ġto Ġapproximate Ġthe Ġreal Ġdata Ġdistribution ĠIN LINE FORM 6 Ġas Ġmuch Ġas Ġpossible . </s> To Ġcompare Ġthe Ġdifferent ĠG AN Ġlosses , ĠKur ach Ġk ur ach 2018 gan Ġtakes Ġa Ġsober Ġview Ġof Ġthe Ġcurrent Ġstate Ġof ĠG AN Ġand Ġsuggests Ġthat Ġthe ĠJ ansen - Sh annon Ġdivergence Ġused Ġin ĠB IB REF 3 Ġperforms Ġmore Ġstable Ġthan Ġvariant Ġobjectives . ĠBesides , ĠKur ach Ġalso Ġadvocates Ġthat Ġthe Ġgradient Ġpenalty Ġ( GP ) Ġregular ization Ġdevised Ġin ĠB IB REF 8 Ġwill Ġfurther Ġimprove Ġthe Ġstability Ġof Ġthe Ġmodel . ĠThus , Ġthe Ġobjective Ġfunction Ġof Ġthe Ġproposed ĠA EM Ġis Ġdefined Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġdenotes Ġthe Ġdiscrim inator Ġloss , ĠIN LINE FORM 1 Ġrepresents Ġthe Ġgradient Ġpenalty Ġregular ization Ġloss , ĠIN LINE FORM 2 Ġis Ġthe Ġgradient Ġpenalty Ġcoefficient Ġwhich Ġtrade - off Ġthe Ġtwo Ġcomponents Ġof Ġobjective , ĠIN LINE FORM 3 Ġcould Ġbe Ġobtained Ġby Ġsampling Ġuniformly Ġalong Ġa Ġstraight Ġline Ġbetween ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġ, ĠIN LINE FORM 6 Ġdenotes Ġthe Ġcorresponding Ġdistribution . </s> The Ġtraining Ġprocedure Ġof ĠA EM Ġis Ġpresented Ġin ĠAl gorithm ĠSEC REF 15 Ġ, Ġwhere ĠIN LINE FORM 0 Ġis Ġthe Ġevent Ġnumber , ĠIN LINE FORM 1 Ġdenotes Ġthe Ġnumber Ġof Ġdiscrim inator Ġiterations Ġper Ġgenerator Ġiteration , ĠIN LINE FORM 2 Ġis Ġthe Ġbatch Ġsize , ĠIN LINE FORM 3 Ġrepresents Ġthe Ġlearning Ġrate , ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġare Ġhyper - param eters Ġof ĠAdam ĠB IB REF 11 Ġ, ĠIN LINE FORM 6 Ġdenotes ĠIN LINE FORM 7 Ġ. ĠIn Ġthis Ġpaper , Ġwe Ġset ĠIN LINE FORM 8 Ġ, ĠIN LINE FORM 9 Ġ, ĠIN LINE FORM 10 Ġ. ĠMoreover , ĠIN LINE FORM 11 Ġ, ĠIN LINE FORM 12 Ġand ĠIN LINE FORM 13 Ġare Ġset Ġas Ġ0 . 0002 , Ġ0 . 5 Ġand Ġ0 . 999 . </s> [ ! h ] ĠTraining Ġprocedure Ġfor ĠA EM Ġ[ 1 ] ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġ, ĠIN LINE FORM 4 Ġ, ĠIN LINE FORM 5 Ġ, ĠIN LINE FORM 6 Ġthe Ġtrained ĠIN LINE FORM 7 Ġand ĠIN LINE FORM 8 Ġ. ĠInitial ĠIN LINE FORM 9 Ġparameters ĠIN LINE FORM 10 Ġand ĠIN LINE FORM 11 Ġparameter ĠIN LINE FORM 12 ĠIN LINE FORM 13 Ġhas Ġnot Ġconver ged ĠIN LINE FORM 14 ĠIN LINE FORM 15 ĠSample ĠIN LINE FORM 16 Ġ, ĠSample Ġa Ġrandom ĠIN LINE FORM 17 ĠSample Ġa Ġrandom Ġnumber ĠIN LINE FORM 18 ĠIN LINE FORM 19 ĠIN LINE FORM 20 ĠIN LINE FORM 21 ĠIN LINE FORM 22 ĠIN LINE FORM 23 ĠIN LINE FORM 24 ĠSample ĠIN LINE FORM 25 Ġnoise ĠIN LINE FORM 26 ĠIN LINE FORM 27 </s> Event ĠGeneration </s> After Ġthe Ġmodel Ġtraining , Ġthe Ġgenerator ĠIN LINE FORM 0 Ġlearns Ġthe Ġmapping Ġfunction Ġbetween Ġthe Ġdocument - event Ġdistribution Ġand Ġthe Ġdocument - level Ġevent - related Ġword Ġdistributions Ġ( entity , Ġlocation , Ġkeyword Ġand Ġdate ). ĠIn Ġother Ġwords , Ġwith Ġan Ġevent Ġdistribution ĠIN LINE FORM 1 Ġas Ġinput , ĠIN LINE FORM 2 Ġcould Ġgenerate Ġthe Ġcorresponding Ġentity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution . </s> In ĠA EM , Ġwe Ġemploy Ġevent Ġseed ĠIN LINE FORM 0 Ġ, Ġan ĠIN LINE FORM 1 Ġ- dimensional Ġvector Ġwith Ġone - hot Ġencoding , Ġto Ġgenerate Ġthe Ġevent Ġrelated Ġword Ġdistributions . ĠFor Ġexample , Ġin Ġten Ġevent Ġsetting , ĠIN LINE FORM 2 Ġrepresents Ġthe Ġevent Ġseed Ġof Ġthe Ġfirst Ġevent . ĠWith Ġthe Ġevent Ġseed ĠIN LINE FORM 3 Ġas Ġinput , Ġthe Ġcorresponding Ġdistributions Ġcould Ġbe Ġgenerated Ġby ĠIN LINE FORM 4 Ġbased Ġon Ġthe Ġequation Ġbelow : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġdenote Ġthe Ġentity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution Ġof Ġthe Ġfirst Ġevent Ġrespectively . </s> Exper iments </s> In Ġthis Ġsection , Ġwe Ġfirst ly Ġdescribe Ġthe Ġdatasets Ġand Ġbaseline Ġapproaches Ġused Ġin Ġour Ġexperiments Ġand Ġthen Ġpresent Ġthe Ġexperimental Ġresults . </s> Exper imental ĠSetup </s> To Ġvalidate Ġthe Ġeffectiveness Ġof ĠA EM Ġfor Ġextracting Ġevents Ġfrom Ġsocial Ġmedia Ġ( e . g . ĠTwitter ) Ġand Ġnews Ġmedia Ġsites Ġ( e . g . ĠGoogle Ġnews ), Ġthree Ġdatasets Ġ( F SD ĠB IB REF 12 Ġ, ĠTwitter , Ġand ĠGoogle Ġdatasets ) Ġare Ġemployed . ĠDetails Ġare Ġsummarized Ġbelow : </s> F SD Ġdataset Ġ( social Ġmedia ) Ġis Ġthe Ġfirst Ġstory Ġdetection Ġdataset Ġcontaining Ġ2 , 499 Ġtweets . ĠWe Ġfilter Ġout Ġevents Ġmentioned Ġin Ġless Ġthan Ġ15 Ġtweets Ġsince Ġevents Ġmentioned Ġin Ġvery Ġfew Ġtweets Ġare Ġless Ġlikely Ġto Ġbe Ġsignificant . ĠThe Ġfinal Ġdataset Ġcontains Ġ2 , 453 Ġtweets Ġannot ated Ġwith Ġ20 Ġevents . </s> Twitter Ġdataset Ġ( social Ġmedia ) Ġis Ġcollected Ġfrom Ġtweets Ġpublished Ġin Ġthe Ġmonth Ġof ĠDecember Ġin Ġ2010 Ġusing ĠTwitter Ġstreaming ĠAPI . ĠIt Ġcontains Ġ1 , 000 Ġtweets Ġannot ated Ġwith Ġ20 Ġevents . </s> Google Ġdataset Ġ( news Ġarticle ) Ġis Ġa Ġsubset Ġof ĠGD EL T ĠEvent ĠDatabase ĠIN LINE FORM 0 Ġ, Ġdocuments Ġare Ġretrieved Ġby Ġevent Ġrelated Ġwords . ĠFor Ġexample , Ġdocuments Ġwhich Ġcontain Ġ` mal ays ia ', Ġ` air line ', Ġ` search ' Ġand Ġ` plane ' Ġare Ġretrieved Ġfor Ġevent ĠMH 370 . ĠBy Ġcombining Ġ30 Ġevents Ġrelated Ġdocuments , Ġthe Ġdataset Ġcontains Ġ11 , 909 Ġnews Ġarticles . </s> We Ġchoose Ġthe Ġfollowing Ġthree Ġmodels Ġas Ġthe Ġbas elines : </s> K - me ans Ġis Ġa Ġwell Ġknown Ġdata Ġclust ering Ġalgorithm , Ġwe Ġimplement Ġthe Ġalgorithm Ġusing Ġsk learn Ġtool box , Ġand Ġrepresent Ġdocuments Ġusing Ġbag - of - words Ġweighted Ġby ĠTF - ID F . </s> LE M ĠB IB REF 13 Ġis Ġa ĠBay esian Ġmodeling Ġapproach Ġfor Ġopen - domain Ġevent Ġextraction . ĠIt Ġtreats Ġan Ġevent Ġas Ġa Ġlatent Ġvariable Ġand Ġmodels Ġthe Ġgeneration Ġof Ġan Ġevent Ġas Ġa Ġjoint Ġdistribution Ġof Ġits Ġindividual Ġevent Ġelements . ĠWe Ġimplement Ġthe Ġalgorithm Ġwith Ġthe Ġdefault Ġconfiguration . </s> DP EM M ĠB IB REF 14 Ġis Ġa Ġnon - param etric Ġmixture Ġmodel Ġfor Ġevent Ġextraction . ĠIt Ġaddresses Ġthe Ġlimitation Ġof ĠL EM Ġthat Ġthe Ġnumber Ġof Ġevents Ġshould Ġbe Ġknown Ġbeforehand . ĠWe Ġimplement Ġthe Ġmodel Ġwith Ġthe Ġdefault Ġconfiguration . </s> For Ġsocial Ġmedia Ġtext Ġcorpus Ġ( F SD Ġand ĠTwitter ), Ġa Ġnamed Ġentity Ġtag ger Ġspecifically Ġbuilt Ġfor ĠTwitter Ġis Ġused Ġto Ġextract Ġnamed Ġentities Ġincluding Ġlocations Ġfrom Ġtweets . ĠA ĠTwitter ĠPart - of - Spe ech Ġ( POS ) Ġtag ger ĠB IB REF 15 Ġis Ġused Ġfor ĠPOS Ġtagging Ġand Ġonly Ġwords Ġtagged Ġwith Ġnoun s , Ġverbs Ġand Ġadject ives Ġare Ġretained Ġas Ġkeywords . ĠFor Ġthe ĠGoogle Ġdataset , Ġwe Ġuse Ġthe ĠStanford ĠNamed ĠEntity ĠRecogn izer Ġto Ġidentify Ġthe Ġnamed Ġentities Ġ( organ ization , Ġlocation Ġand Ġperson ). ĠDue Ġto Ġthe Ġ` date ' Ġinformation Ġnot Ġbeing Ġprovided Ġin Ġthe ĠGoogle Ġdataset , Ġwe Ġfurther Ġdivide Ġthe Ġnon - location Ġnamed Ġentities Ġinto Ġtwo Ġcategories Ġ( ` person ' Ġand Ġ` organ ization ') Ġand Ġemploy Ġa Ġquadru ple Ġ< organ ization , Ġlocation , Ġperson , Ġkeyword > Ġto Ġdenote Ġan Ġevent Ġin Ġnews Ġarticles . ĠWe Ġalso Ġremove Ġcommon Ġstop words Ġand Ġonly Ġkeep Ġthe Ġrecognized Ġnamed Ġentities Ġand Ġthe Ġtokens Ġwhich Ġare Ġverbs , Ġnoun s Ġor Ġadject ives . </s> Exper imental ĠResults </s> To Ġevaluate Ġthe Ġperformance Ġof Ġthe Ġproposed Ġapproach , Ġwe Ġuse Ġthe Ġevaluation Ġmetrics Ġsuch Ġas Ġprecision , Ġrecall Ġand ĠF - me asure . ĠPrecision Ġis Ġdefined Ġas Ġthe Ġproportion Ġof Ġthe Ġcorrectly Ġidentified Ġevents Ġout Ġof Ġthe Ġmodel Ġgenerated Ġevents . ĠRecall Ġis Ġdefined Ġas Ġthe Ġproportion Ġof Ġcorrectly Ġidentified Ġtrue Ġevents . ĠFor Ġcalculating Ġthe Ġprecision Ġof Ġthe Ġ4 - t uple , Ġwe Ġuse Ġfollowing Ġcriteria : </s> ( 1 ) ĠDo Ġthe Ġentity / organ ization , Ġlocation , Ġdate / person Ġand Ġkeyword Ġthat Ġwe Ġhave Ġextracted Ġrefer Ġto Ġthe Ġsame Ġevent ? </s> ( 2 ) ĠIf Ġthe Ġextracted Ġrepresentation Ġcontains Ġkeywords , Ġare Ġthey Ġinformative Ġenough Ġto Ġtell Ġus Ġwhat Ġhappened ? </s> Table ĠT AB REF 35 Ġshows Ġthe Ġevent Ġextraction Ġresults Ġon Ġthe Ġthree Ġdatasets . ĠThe Ġstatistics Ġare Ġobtained Ġwith Ġthe Ġdefault Ġparameter Ġsetting Ġthat ĠIN LINE FORM 0 Ġis Ġset Ġto Ġ5 , Ġnumber Ġof Ġhidden Ġunits ĠIN LINE FORM 1 Ġis Ġset Ġto Ġ200 , Ġand ĠIN LINE FORM 2 Ġcontains Ġthree Ġfully - connected Ġlayers . ĠThe Ġevent Ġnumber ĠIN LINE FORM 3 Ġfor Ġthree Ġdatasets Ġare Ġset Ġto Ġ25 , Ġ25 Ġand Ġ35 , Ġrespectively . ĠThe Ġexamples Ġof Ġextracted Ġevents Ġare Ġshown Ġin ĠTable . ĠT AB REF 36 Ġ. </s> It Ġcan Ġbe Ġobserved Ġthat ĠK - me ans Ġperforms Ġthe Ġworst Ġover Ġall Ġthree Ġdatasets . ĠOn Ġthe Ġsocial Ġmedia Ġdatasets , ĠA EM Ġout po er forms Ġboth ĠL EM Ġand ĠDP EM M Ġby Ġ6 . 5 % Ġand Ġ1 . 7 % Ġrespectively Ġin ĠF - me asure Ġon Ġthe ĠF SD Ġdataset , Ġand Ġ4 . 4 % Ġand Ġ3 . 7 % Ġin ĠF - me asure Ġon Ġthe ĠTwitter Ġdataset . ĠWe Ġcan Ġalso Ġobserve Ġthat Ġapart Ġfrom ĠK - me ans , Ġall Ġthe Ġapproaches Ġperform Ġworse Ġon Ġthe ĠTwitter Ġdataset Ġcompared Ġto ĠF SD , Ġpossibly Ġdue Ġto Ġthe Ġlimited Ġsize Ġof Ġthe ĠTwitter Ġdataset . ĠMoreover , Ġon Ġthe ĠGoogle Ġdataset , Ġthe Ġproposed ĠA EM Ġperforms Ġsignificantly Ġbetter Ġthan ĠL EM Ġand ĠDP EM M . ĠIt Ġimproves Ġupon ĠL EM Ġby Ġ15 . 5 % Ġand Ġupon ĠDP EM M Ġby Ġmore Ġthan Ġ30 % Ġin ĠF - me asure . ĠThis Ġis Ġbecause : Ġ( 1 ) Ġthe Ġassumption Ġmade Ġby ĠL EM Ġand ĠDP EM M Ġthat Ġall Ġwords Ġin Ġa Ġdocument Ġare Ġgenerated Ġfrom Ġa Ġsingle Ġevent Ġis Ġnot Ġsuitable Ġfor Ġlong Ġtext Ġsuch Ġas Ġnews Ġarticles ; Ġ( 2 ) ĠDP EM M Ġgenerates Ġtoo Ġmany Ġirrelevant Ġevents Ġwhich Ġleads Ġto Ġa Ġvery Ġlow Ġprecision Ġscore . ĠOverall , Ġwe Ġsee Ġthe Ġsuperior Ġperformance Ġof ĠA EM Ġacross Ġall Ġdatasets , Ġwith Ġmore Ġsignificant Ġimprovement Ġon Ġthe Ġfor ĠGoogle Ġdatasets Ġ( long Ġtext ). </s> We Ġnext Ġvisualize Ġthe Ġdetected Ġevents Ġbased Ġon Ġthe Ġdiscrim in ative Ġfeatures Ġlearned Ġby Ġthe Ġtrained ĠIN LINE FORM 0 Ġnetwork Ġin ĠA EM . ĠThe Ġt - S NE ĠB IB REF 16 Ġvisualization Ġresults Ġon Ġthe Ġdatasets Ġare Ġshown Ġin ĠFigure ĠFIG REF 19 Ġ. ĠFor Ġclarity , Ġeach Ġsub plot Ġis Ġplotted Ġon Ġa Ġsubset Ġof Ġthe Ġdataset Ġcontaining Ġten Ġrandomly Ġselected Ġevents . ĠIt Ġcan Ġbe Ġobserved Ġthat Ġdocuments Ġdescribing Ġthe Ġsame Ġevent Ġhave Ġbeen Ġgrouped Ġinto Ġthe Ġsame Ġcluster . </s> To Ġfurther Ġevaluate Ġif Ġa Ġvariation Ġof Ġthe Ġparameters ĠIN LINE FORM 0 Ġ( the Ġnumber Ġof Ġdiscrim inator Ġiterations Ġper Ġgenerator Ġiteration ), ĠIN LINE FORM 1 Ġ( the Ġnumber Ġof Ġunits Ġin Ġhidden Ġlayer ) Ġand Ġthe Ġstructure Ġof Ġgenerator ĠIN LINE FORM 2 Ġwill Ġimpact Ġthe Ġextraction Ġperformance , Ġadditional Ġexperiments Ġhave Ġbeen Ġconducted Ġon Ġthe ĠGoogle Ġdataset , Ġwith ĠIN LINE FORM 3 Ġset Ġto Ġ5 , Ġ7 Ġand Ġ10 , ĠIN LINE FORM 4 Ġset Ġto Ġ100 , Ġ150 Ġand Ġ200 , Ġand Ġthree ĠIN LINE FORM 5 Ġstructures Ġ( 3 , Ġ4 Ġand Ġ5 Ġlayers ). ĠThe Ġcomparison Ġresults Ġon Ġprecision , Ġrecall Ġand ĠF - me asure Ġare Ġshown Ġin ĠFigure ĠFIG REF 20 Ġ. ĠFrom Ġthe Ġresults , Ġit Ġcould Ġbe Ġobserved Ġthat ĠA EM Ġwith Ġthe Ġ5 - layer Ġgenerator Ġperforms Ġthe Ġbest Ġand Ġachieves Ġ96 . 7 % Ġin ĠF - me asure , Ġand Ġthe Ġworst ĠF - me asure Ġobtained Ġby ĠA EM Ġis Ġ85 . 7 %. ĠOverall , Ġthe ĠA EM Ġoutper forms Ġall Ġcompared Ġapproaches Ġac ors s Ġvarious Ġparameter Ġsettings , Ġshowing Ġrelatively Ġstable Ġperformance . </s> Finally , Ġwe Ġcompare Ġin ĠFigure ĠFIG REF 37 Ġthe Ġtraining Ġtime Ġrequired Ġfor Ġeach Ġmodel , Ġexcluding Ġthe Ġconstant Ġtime Ġrequired Ġby Ġeach Ġmodel Ġto Ġload Ġthe Ġdata . ĠWe Ġcould Ġobserve Ġthat ĠK - me ans Ġruns Ġfastest Ġamong Ġall Ġfour Ġapproaches . ĠBoth ĠL EM Ġand ĠDP EM M Ġneed Ġto Ġsample Ġthe Ġevent Ġallocation Ġfor Ġeach Ġdocument Ġand Ġupdate Ġthe Ġrelevant Ġcounts Ġduring ĠGibbs Ġsampling Ġwhich Ġare Ġtime Ġconsuming . ĠA EM Ġonly Ġrequires Ġa Ġfraction Ġof Ġthe Ġtraining Ġtime Ġcompared Ġto ĠL EM Ġand ĠDP EM M . ĠMoreover , Ġon Ġa Ġlarger Ġdataset Ġsuch Ġas Ġthe ĠGoogle Ġdataset , ĠA EM Ġappears Ġto Ġbe Ġfar Ġmore Ġefficient Ġcompared Ġto ĠL EM Ġand ĠDP EM M . </s> Con clusions Ġand ĠFuture ĠWork </s> In Ġthis Ġpaper , Ġwe Ġhave Ġproposed Ġa Ġnovel Ġapproach Ġbased Ġon Ġadvers arial Ġtraining Ġto Ġextract Ġthe Ġstructured Ġrepresentation Ġof Ġevents Ġfrom Ġonline Ġtext . ĠThe Ġexperimental Ġcomparison Ġwith Ġthe Ġstate - of - the - art Ġmethods Ġshows Ġthat ĠA EM Ġachieves Ġimproved Ġextraction Ġperformance , Ġespecially Ġon Ġlong Ġtext Ġcorpor a Ġwith Ġan Ġimprovement Ġof Ġ15 % Ġobserved Ġin ĠF - me asure . ĠA EM Ġonly Ġrequires Ġa Ġfraction Ġof Ġtraining Ġtime Ġcompared Ġto Ġexisting ĠBay esian Ġgraphical Ġmodeling Ġapproaches . ĠIn Ġfuture Ġwork , Ġwe Ġwill Ġexplore Ġincorporating Ġexternal Ġknowledge Ġ( e . g . Ġword Ġrelated ness Ġcontained Ġin Ġword Ġembed d ings ) Ġinto Ġthe Ġlearning Ġframework Ġfor Ġevent Ġextraction . ĠBesides , Ġexploring Ġnon param etric Ġneural Ġevent Ġextraction Ġapproaches Ġand Ġdetecting Ġthe Ġevolution Ġof Ġevents Ġover Ġtime Ġfrom Ġnews Ġarticles Ġare Ġother Ġpromising Ġfuture Ġdirections . </s> Acknowled gments </s> We Ġwould Ġlike Ġto Ġthank Ġanonymous Ġreviewers Ġfor Ġtheir Ġvaluable Ġcomments Ġand Ġhelpful Ġsuggestions . ĠThis Ġwork Ġwas Ġfunded Ġby Ġthe ĠNational ĠKey ĠResearch Ġand ĠDevelopment ĠProgram Ġof ĠChina Ġ( 2016 Y FC 130 6 704 ), Ġthe ĠNational ĠNatural ĠScience ĠFoundation Ġof ĠChina Ġ( 617 72 132 ), Ġthe ĠNatural ĠScience ĠFoundation Ġof ĠJiang su ĠProvince Ġof ĠChina Ġ( B K 2016 14 30 ).
Introduction With the increasing popularity of the Internet, online texts provided by social media platform (e.g. Twitter) and news media sites (e.g. Google news) have become important sources of real-world events. Therefore, it is crucial to automatically extract events from online texts. Due to the high variety of events discussed online and the difficulty in obtaining annotated data for training, traditional template-based or supervised learning approaches for event extraction are no longer applicable in dealing with online texts. Nevertheless, newsworthy events are often discussed by many tweets or online news articles. Therefore, the same event could be mentioned by a high volume of redundant tweets or news articles. This property inspires the research community to devise clustering-based models BIBREF0 , BIBREF1 , BIBREF2 to discover new or previously unidentified events without extracting structured representations. To extract structured representations of events such as who did what, when, where and why, Bayesian approaches have made some progress. Assuming that each document is assigned to a single event, which is modeled as a joint distribution over the named entities, the date and the location of the event, and the event-related keywords, Zhou et al. zhou2014simple proposed an unsupervised Latent Event Model (LEM) for open-domain event extraction. To address the limitation that LEM requires the number of events to be pre-set, Zhou et al. zhou2017event further proposed the Dirichlet Process Event Mixture Model (DPEMM) in which the number of events can be learned automatically from data. However, both LEM and DPEMM have two limitations: (1) they assume that all words in a document are generated from a single event which can be represented by a quadruple INLINEFORM0 entity, location, keyword, date INLINEFORM1 . However, long texts such news articles often describe multiple events which clearly violates this assumption; (2) During the inference process of both approaches, the Gibbs sampler needs to compute the conditional posterior distribution and assigns an event for each document. This is time consuming and takes long time to converge. To deal with these limitations, in this paper, we propose the Adversarial-neural Event Model (AEM) based on adversarial training for open-domain event extraction. The principle idea is to use a generator network to learn the projection function between the document-event distribution and four event related word distributions (entity distribution, location distribution, keyword distribution and date distribution). Instead of providing an analytic approximation, AEM uses a discriminator network to discriminate between the reconstructed documents from latent events and the original input documents. This essentially helps the generator to construct a more realistic document from a random noise drawn from a Dirichlet distribution. Due to the flexibility of neural networks, the generator is capable of learning complicated nonlinear distributions. And the supervision signal provided by the discriminator will help generator to capture the event-related patterns. Furthermore, the discriminator also provides low-dimensional discriminative features which can be used to visualize documents and events. The main contributions of the paper are summarized below: Related Work Our work is related to two lines of research, event extraction and Generative Adversarial Nets. Event Extraction Recently there has been much interest in event extraction from online texts, and approaches could be categorized as domain-specific and open-domain event extraction. Domain-specific event extraction often focuses on the specific types of events (e.g. sports events or city events). Panem et al. panem2014structured devised a novel algorithm to extract attribute-value pairs and mapped them to manually generated schemes for extracting the natural disaster events. Similarly, to extract the city-traffic related event, Anantharam et al. anantharam2015extracting viewed the task as a sequential tagging problem and proposed an approach based on the conditional random fields. Zhang zhang2018event proposed an event extraction approach based on imitation learning, especially on inverse reinforcement learning. Open-domain event extraction aims to extract events without limiting the specific types of events. To analyze individual messages and induce a canonical value for each event, Benson et al. benson2011event proposed an approach based on a structured graphical model. By representing an event with a binary tuple which is constituted by a named entity and a date, Ritter et al. ritter2012open employed some statistic to measure the strength of associations between a named entity and a date. The proposed system relies on a supervised labeler trained on annotated data. In BIBREF1 , Abdelhaq et al. developed a real-time event extraction system called EvenTweet, and each event is represented as a triple constituted by time, location and keywords. To extract more information, Wang el al. wang2015seeft developed a system employing the links in tweets and combing tweets with linked articles to identify events. Xia el al. xia2015new combined texts with the location information to detect the events with low spatial and temporal deviations. Zhou et al. zhou2014simple,zhou2017event represented event as a quadruple and proposed two Bayesian models to extract events from tweets. Generative Adversarial Nets As a neural-based generative model, Generative Adversarial Nets BIBREF3 have been extensively researched in natural language processing (NLP) community. For text generation, the sequence generative adversarial network (SeqGAN) proposed in BIBREF4 incorporated a policy gradient strategy to optimize the generation process. Based on the policy gradient, Lin et al. lin2017adversarial proposed RankGAN to capture the rich structures of language by ranking and analyzing a collection of human-written and machine-written sentences. To overcome mode collapse when dealing with discrete data, Fedus et al. fedus2018maskgan proposed MaskGAN which used an actor-critic conditional GAN to fill in missing text conditioned on the surrounding context. Along this line, Wang et al. wang2018sentigan proposed SentiGAN to generate texts of different sentiment labels. Besides, Li et al. li2018learning improved the performance of semi-supervised text classification using adversarial training, BIBREF5 , BIBREF6 designed GAN-based models for distance supervision relation extraction. Although various GAN based approaches have been explored for many applications, none of these approaches tackles open-domain event extraction from online texts. We propose a novel GAN-based event extraction model called AEM. Compared with the previous models, AEM has the following differences: (1) Unlike most GAN-based text generation approaches, a generator network is employed in AEM to learn the projection function between an event distribution and the event-related word distributions (entity, location, keyword, date). The learned generator captures event-related patterns rather than generating text sequence; (2) Different from LEM and DPEMM, AEM uses a generator network to capture the event-related patterns and is able to mine events from different text sources (short and long). Moreover, unlike traditional inference procedure, such as Gibbs sampling used in LEM and DPEMM, AEM could extract the events more efficiently due to the CUDA acceleration; (3) The discriminative features learned by the discriminator of AEM provide a straightforward way to visualize the extracted events. Methodology We describe Adversarial-neural Event Model (AEM) in this section. An event is represented as a quadruple < INLINEFORM0 >, where INLINEFORM1 stands for non-location named entities, INLINEFORM2 for a location, INLINEFORM3 for event-related keywords, INLINEFORM4 for a date, and each component in the tuple is represented by component-specific representative words. AEM is constituted by three components: (1) The document representation module, as shown at the top of Figure FIGREF4 , defines a document representation approach which converts an input document from the online text corpus into INLINEFORM0 which captures the key event elements; (2) The generator INLINEFORM1 , as shown in the lower-left part of Figure FIGREF4 , generates a fake document INLINEFORM2 which is constituted by four multinomial distributions using an event distribution INLINEFORM3 drawn from a Dirichlet distribution as input; (3) The discriminator INLINEFORM4 , as shown in the lower-right part of Figure FIGREF4 , distinguishes the real documents from the fake ones and its output is subsequently employed as a learning signal to update the INLINEFORM5 and INLINEFORM6 . The details of each component are presented below. Document Representation Each document INLINEFORM0 in a given corpus INLINEFORM1 is represented as a concatenation of 4 multinomial distributions which are entity distribution ( INLINEFORM2 ), location distribution ( INLINEFORM3 ), keyword distribution ( INLINEFORM4 ) and date distribution ( INLINEFORM5 ) of the document. As four distributions are calculated in a similar way, we only describe the computation of the entity distribution below as an example. The entity distribution INLINEFORM0 is represented by a normalized INLINEFORM1 -dimensional vector weighted by TF-IDF, and it is calculated as: INLINEFORM2 where INLINEFORM0 is the pseudo corpus constructed by removing all non-entity words from INLINEFORM1 , INLINEFORM2 is the total number of distinct entities in a corpus, INLINEFORM3 denotes the number of INLINEFORM4 -th entity appeared in document INLINEFORM5 , INLINEFORM6 represents the number of documents in the corpus, and INLINEFORM7 is the number of documents that contain INLINEFORM8 -th entity, and the obtained INLINEFORM9 denotes the relevance between INLINEFORM10 -th entity and document INLINEFORM11 . Similarly, location distribution INLINEFORM0 , keyword distribution INLINEFORM1 and date distribution INLINEFORM2 of INLINEFORM3 could be calculated in the same way, and the dimensions of these distributions are denoted as INLINEFORM4 , INLINEFORM5 and INLINEFORM6 , respectively. Finally, each document INLINEFORM7 in the corpus is represented by a INLINEFORM8 -dimensional ( INLINEFORM9 = INLINEFORM10 + INLINEFORM11 + INLINEFORM12 + INLINEFORM13 ) vector INLINEFORM14 by concatenating four computed distributions. Network Architecture The generator network INLINEFORM0 is designed to learn the projection function between the document-event distribution INLINEFORM1 and the four document-level word distributions (entity distribution, location distribution, keyword distribution and date distribution). More concretely, INLINEFORM0 consists of a INLINEFORM1 -dimensional document-event distribution layer, INLINEFORM2 -dimensional hidden layer and INLINEFORM3 -dimensional event-related word distribution layer. Here, INLINEFORM4 denotes the event number, INLINEFORM5 is the number of units in the hidden layer, INLINEFORM6 is the vocabulary size and equals to INLINEFORM7 + INLINEFORM8 + INLINEFORM9 + INLINEFORM10 . As shown in Figure FIGREF4 , INLINEFORM11 firstly employs a random document-event distribution INLINEFORM12 as an input. To model the multinomial property of the document-event distribution, INLINEFORM13 is drawn from a Dirichlet distribution parameterized with INLINEFORM14 which is formulated as: DISPLAYFORM0 where INLINEFORM0 is the hyper-parameter of the dirichlet distribution, INLINEFORM1 is the number of events which should be set in AEM, INLINEFORM2 , INLINEFORM3 represents the proportion of event INLINEFORM4 in the document and INLINEFORM5 . Subsequently, INLINEFORM0 transforms INLINEFORM1 into a INLINEFORM2 -dimensional hidden space using a linear layer followed by layer normalization, and the transformation is defined as: DISPLAYFORM0 where INLINEFORM0 represents the weight matrix of hidden layer, and INLINEFORM1 denotes the bias term, INLINEFORM2 is the parameter of LeakyReLU activation and is set to 0.1, INLINEFORM3 and INLINEFORM4 denote the normalized hidden states and the outputs of the hidden layer, and INLINEFORM5 represents the layer normalization. Then, to project INLINEFORM0 into four document-level event related word distributions ( INLINEFORM1 , INLINEFORM2 , INLINEFORM3 and INLINEFORM4 shown in Figure FIGREF4 ), four subnets (each contains a linear layer, a batch normalization layer and a softmax layer) are employed in INLINEFORM5 . And the exact transformation is based on the formulas below: DISPLAYFORM0 where INLINEFORM0 means softmax layer, INLINEFORM1 , INLINEFORM2 , INLINEFORM3 and INLINEFORM4 denote the weight matrices of the linear layers in subnets, INLINEFORM5 , INLINEFORM6 , INLINEFORM7 and INLINEFORM8 represent the corresponding bias terms, INLINEFORM9 , INLINEFORM10 , INLINEFORM11 and INLINEFORM12 are state vectors. INLINEFORM13 , INLINEFORM14 , INLINEFORM15 and INLINEFORM16 denote the generated entity distribution, location distribution, keyword distribution and date distribution, respectively, that correspond to the given event distribution INLINEFORM17 . And each dimension represents the relevance between corresponding entity/location/keyword/date term and the input event distribution. Finally, four generated distributions are concatenated to represent the generated document INLINEFORM0 corresponding to the input INLINEFORM1 : DISPLAYFORM0 The discriminator network INLINEFORM0 is designed as a fully-connected network which contains an input layer, a discriminative feature layer (discriminative features are employed for event visualization) and an output layer. In AEM, INLINEFORM1 uses fake document INLINEFORM2 and real document INLINEFORM3 as input and outputs the signal INLINEFORM4 to indicate the source of the input data (lower value denotes that INLINEFORM5 is prone to predict the input data as a fake document and vice versa). As have previously been discussed in BIBREF7 , BIBREF8 , lipschitz continuity of INLINEFORM0 network is crucial to the training of the GAN-based approaches. To ensure the lipschitz continuity of INLINEFORM1 , we employ the spectral normalization technique BIBREF9 . More concretely, for each linear layer INLINEFORM2 (bias term is omitted for simplicity) in INLINEFORM3 , the weight matrix INLINEFORM4 is normalized by INLINEFORM5 . Here, INLINEFORM6 is the spectral norm of the weight matrix INLINEFORM7 with the definition below: DISPLAYFORM0 which is equivalent to the largest singular value of INLINEFORM0 . The weight matrix INLINEFORM1 is then normalized using: DISPLAYFORM0 Obviously, the normalized weight matrix INLINEFORM0 satisfies that INLINEFORM1 and further ensures the lipschitz continuity of the INLINEFORM2 network BIBREF9 . To reduce the high cost of computing spectral norm INLINEFORM3 using singular value decomposition at each iteration, we follow BIBREF10 and employ the power iteration method to estimate INLINEFORM4 instead. With this substitution, the spectral norm can be estimated with very small additional computational time. Objective and Training Procedure The real document INLINEFORM0 and fake document INLINEFORM1 shown in Figure FIGREF4 could be viewed as random samples from two distributions INLINEFORM2 and INLINEFORM3 , and each of them is a joint distribution constituted by four Dirichlet distributions (corresponding to entity distribution, location distribution, keyword distribution and date distribution). The training objective of AEM is to let the distribution INLINEFORM4 (produced by INLINEFORM5 network) to approximate the real data distribution INLINEFORM6 as much as possible. To compare the different GAN losses, Kurach kurach2018gan takes a sober view of the current state of GAN and suggests that the Jansen-Shannon divergence used in BIBREF3 performs more stable than variant objectives. Besides, Kurach also advocates that the gradient penalty (GP) regularization devised in BIBREF8 will further improve the stability of the model. Thus, the objective function of the proposed AEM is defined as: DISPLAYFORM0 where INLINEFORM0 denotes the discriminator loss, INLINEFORM1 represents the gradient penalty regularization loss, INLINEFORM2 is the gradient penalty coefficient which trade-off the two components of objective, INLINEFORM3 could be obtained by sampling uniformly along a straight line between INLINEFORM4 and INLINEFORM5 , INLINEFORM6 denotes the corresponding distribution. The training procedure of AEM is presented in Algorithm SECREF15 , where INLINEFORM0 is the event number, INLINEFORM1 denotes the number of discriminator iterations per generator iteration, INLINEFORM2 is the batch size, INLINEFORM3 represents the learning rate, INLINEFORM4 and INLINEFORM5 are hyper-parameters of Adam BIBREF11 , INLINEFORM6 denotes INLINEFORM7 . In this paper, we set INLINEFORM8 , INLINEFORM9 , INLINEFORM10 . Moreover, INLINEFORM11 , INLINEFORM12 and INLINEFORM13 are set as 0.0002, 0.5 and 0.999. [!h] Training procedure for AEM [1] INLINEFORM0 , INLINEFORM1 , INLINEFORM2 , INLINEFORM3 , INLINEFORM4 , INLINEFORM5 , INLINEFORM6 the trained INLINEFORM7 and INLINEFORM8 . Initial INLINEFORM9 parameters INLINEFORM10 and INLINEFORM11 parameter INLINEFORM12 INLINEFORM13 has not converged INLINEFORM14 INLINEFORM15 Sample INLINEFORM16 , Sample a random INLINEFORM17 Sample a random number INLINEFORM18 INLINEFORM19 INLINEFORM20 INLINEFORM21 INLINEFORM22 INLINEFORM23 INLINEFORM24 Sample INLINEFORM25 noise INLINEFORM26 INLINEFORM27 Event Generation After the model training, the generator INLINEFORM0 learns the mapping function between the document-event distribution and the document-level event-related word distributions (entity, location, keyword and date). In other words, with an event distribution INLINEFORM1 as input, INLINEFORM2 could generate the corresponding entity distribution, location distribution, keyword distribution and date distribution. In AEM, we employ event seed INLINEFORM0 , an INLINEFORM1 -dimensional vector with one-hot encoding, to generate the event related word distributions. For example, in ten event setting, INLINEFORM2 represents the event seed of the first event. With the event seed INLINEFORM3 as input, the corresponding distributions could be generated by INLINEFORM4 based on the equation below: DISPLAYFORM0 where INLINEFORM0 , INLINEFORM1 , INLINEFORM2 and INLINEFORM3 denote the entity distribution, location distribution, keyword distribution and date distribution of the first event respectively. Experiments In this section, we firstly describe the datasets and baseline approaches used in our experiments and then present the experimental results. Experimental Setup To validate the effectiveness of AEM for extracting events from social media (e.g. Twitter) and news media sites (e.g. Google news), three datasets (FSD BIBREF12 , Twitter, and Google datasets) are employed. Details are summarized below: FSD dataset (social media) is the first story detection dataset containing 2,499 tweets. We filter out events mentioned in less than 15 tweets since events mentioned in very few tweets are less likely to be significant. The final dataset contains 2,453 tweets annotated with 20 events. Twitter dataset (social media) is collected from tweets published in the month of December in 2010 using Twitter streaming API. It contains 1,000 tweets annotated with 20 events. Google dataset (news article) is a subset of GDELT Event Database INLINEFORM0 , documents are retrieved by event related words. For example, documents which contain `malaysia', `airline', `search' and `plane' are retrieved for event MH370. By combining 30 events related documents, the dataset contains 11,909 news articles. We choose the following three models as the baselines: K-means is a well known data clustering algorithm, we implement the algorithm using sklearn toolbox, and represent documents using bag-of-words weighted by TF-IDF. LEM BIBREF13 is a Bayesian modeling approach for open-domain event extraction. It treats an event as a latent variable and models the generation of an event as a joint distribution of its individual event elements. We implement the algorithm with the default configuration. DPEMM BIBREF14 is a non-parametric mixture model for event extraction. It addresses the limitation of LEM that the number of events should be known beforehand. We implement the model with the default configuration. For social media text corpus (FSD and Twitter), a named entity tagger specifically built for Twitter is used to extract named entities including locations from tweets. A Twitter Part-of-Speech (POS) tagger BIBREF15 is used for POS tagging and only words tagged with nouns, verbs and adjectives are retained as keywords. For the Google dataset, we use the Stanford Named Entity Recognizer to identify the named entities (organization, location and person). Due to the `date' information not being provided in the Google dataset, we further divide the non-location named entities into two categories (`person' and `organization') and employ a quadruple <organization, location, person, keyword> to denote an event in news articles. We also remove common stopwords and only keep the recognized named entities and the tokens which are verbs, nouns or adjectives. Experimental Results To evaluate the performance of the proposed approach, we use the evaluation metrics such as precision, recall and F-measure. Precision is defined as the proportion of the correctly identified events out of the model generated events. Recall is defined as the proportion of correctly identified true events. For calculating the precision of the 4-tuple, we use following criteria: (1) Do the entity/organization, location, date/person and keyword that we have extracted refer to the same event? (2) If the extracted representation contains keywords, are they informative enough to tell us what happened? Table TABREF35 shows the event extraction results on the three datasets. The statistics are obtained with the default parameter setting that INLINEFORM0 is set to 5, number of hidden units INLINEFORM1 is set to 200, and INLINEFORM2 contains three fully-connected layers. The event number INLINEFORM3 for three datasets are set to 25, 25 and 35, respectively. The examples of extracted events are shown in Table. TABREF36 . It can be observed that K-means performs the worst over all three datasets. On the social media datasets, AEM outpoerforms both LEM and DPEMM by 6.5% and 1.7% respectively in F-measure on the FSD dataset, and 4.4% and 3.7% in F-measure on the Twitter dataset. We can also observe that apart from K-means, all the approaches perform worse on the Twitter dataset compared to FSD, possibly due to the limited size of the Twitter dataset. Moreover, on the Google dataset, the proposed AEM performs significantly better than LEM and DPEMM. It improves upon LEM by 15.5% and upon DPEMM by more than 30% in F-measure. This is because: (1) the assumption made by LEM and DPEMM that all words in a document are generated from a single event is not suitable for long text such as news articles; (2) DPEMM generates too many irrelevant events which leads to a very low precision score. Overall, we see the superior performance of AEM across all datasets, with more significant improvement on the for Google datasets (long text). We next visualize the detected events based on the discriminative features learned by the trained INLINEFORM0 network in AEM. The t-SNE BIBREF16 visualization results on the datasets are shown in Figure FIGREF19 . For clarity, each subplot is plotted on a subset of the dataset containing ten randomly selected events. It can be observed that documents describing the same event have been grouped into the same cluster. To further evaluate if a variation of the parameters INLINEFORM0 (the number of discriminator iterations per generator iteration), INLINEFORM1 (the number of units in hidden layer) and the structure of generator INLINEFORM2 will impact the extraction performance, additional experiments have been conducted on the Google dataset, with INLINEFORM3 set to 5, 7 and 10, INLINEFORM4 set to 100, 150 and 200, and three INLINEFORM5 structures (3, 4 and 5 layers). The comparison results on precision, recall and F-measure are shown in Figure FIGREF20 . From the results, it could be observed that AEM with the 5-layer generator performs the best and achieves 96.7% in F-measure, and the worst F-measure obtained by AEM is 85.7%. Overall, the AEM outperforms all compared approaches acorss various parameter settings, showing relatively stable performance. Finally, we compare in Figure FIGREF37 the training time required for each model, excluding the constant time required by each model to load the data. We could observe that K-means runs fastest among all four approaches. Both LEM and DPEMM need to sample the event allocation for each document and update the relevant counts during Gibbs sampling which are time consuming. AEM only requires a fraction of the training time compared to LEM and DPEMM. Moreover, on a larger dataset such as the Google dataset, AEM appears to be far more efficient compared to LEM and DPEMM. Conclusions and Future Work In this paper, we have proposed a novel approach based on adversarial training to extract the structured representation of events from online text. The experimental comparison with the state-of-the-art methods shows that AEM achieves improved extraction performance, especially on long text corpora with an improvement of 15% observed in F-measure. AEM only requires a fraction of training time compared to existing Bayesian graphical modeling approaches. In future work, we will explore incorporating external knowledge (e.g. word relatedness contained in word embeddings) into the learning framework for event extraction. Besides, exploring nonparametric neural event extraction approaches and detecting the evolution of events over time from news articles are other promising future directions. Acknowledgments We would like to thank anonymous reviewers for their valuable comments and helpful suggestions. This work was funded by the National Key Research and Development Program of China (2016YFC1306704), the National Natural Science Foundation of China (61772132), the Natural Science Foundation of Jiangsu Province of China (BK20161430).
Do they report results only on English data?
f3c204723da53c7c8ef4dc1018ffbee545e81056
Do Ġthey Ġreport Ġresults Ġonly Ġon ĠEnglish Ġdata ?
Un answer able
<s> Do Ġthey Ġreport Ġresults Ġonly Ġon ĠEnglish Ġdata ? </s> Introduction </s> With Ġthe Ġincreasing Ġpopularity Ġof Ġthe ĠInternet , Ġonline Ġtexts Ġprovided Ġby Ġsocial Ġmedia Ġplatform Ġ( e . g . ĠTwitter ) Ġand Ġnews Ġmedia Ġsites Ġ( e . g . ĠGoogle Ġnews ) Ġhave Ġbecome Ġimportant Ġsources Ġof Ġreal - world Ġevents . ĠTherefore , Ġit Ġis Ġcrucial Ġto Ġautomatically Ġextract Ġevents Ġfrom Ġonline Ġtexts . </s> Due Ġto Ġthe Ġhigh Ġvariety Ġof Ġevents Ġdiscussed Ġonline Ġand Ġthe Ġdifficulty Ġin Ġobtaining Ġannot ated Ġdata Ġfor Ġtraining , Ġtraditional Ġtemplate - based Ġor Ġsupervised Ġlearning Ġapproaches Ġfor Ġevent Ġextraction Ġare Ġno Ġlonger Ġapplicable Ġin Ġdealing Ġwith Ġonline Ġtexts . ĠNevertheless , Ġnews worthy Ġevents Ġare Ġoften Ġdiscussed Ġby Ġmany Ġtweets Ġor Ġonline Ġnews Ġarticles . ĠTherefore , Ġthe Ġsame Ġevent Ġcould Ġbe Ġmentioned Ġby Ġa Ġhigh Ġvolume Ġof Ġredundant Ġtweets Ġor Ġnews Ġarticles . ĠThis Ġproperty Ġinspires Ġthe Ġresearch Ġcommunity Ġto Ġdevise Ġclust ering - based Ġmodels ĠB IB REF 0 Ġ, ĠB IB REF 1 Ġ, ĠB IB REF 2 Ġto Ġdiscover Ġnew Ġor Ġpreviously Ġunidentified Ġevents Ġwithout Ġextracting Ġstructured Ġrepresentations . </s> To Ġextract Ġstructured Ġrepresentations Ġof Ġevents Ġsuch Ġas Ġwho Ġdid Ġwhat , Ġwhen , Ġwhere Ġand Ġwhy , ĠBay esian Ġapproaches Ġhave Ġmade Ġsome Ġprogress . ĠAssuming Ġthat Ġeach Ġdocument Ġis Ġassigned Ġto Ġa Ġsingle Ġevent , Ġwhich Ġis Ġmodeled Ġas Ġa Ġjoint Ġdistribution Ġover Ġthe Ġnamed Ġentities , Ġthe Ġdate Ġand Ġthe Ġlocation Ġof Ġthe Ġevent , Ġand Ġthe Ġevent - related Ġkeywords , ĠZhou Ġet Ġal . Ġz hou 2014 simple Ġproposed Ġan Ġun super vised ĠLat ent ĠEvent ĠModel Ġ( LE M ) Ġfor Ġopen - domain Ġevent Ġextraction . ĠTo Ġaddress Ġthe Ġlimitation Ġthat ĠL EM Ġrequires Ġthe Ġnumber Ġof Ġevents Ġto Ġbe Ġpre - set , ĠZhou Ġet Ġal . Ġz hou 2017 event Ġfurther Ġproposed Ġthe ĠDir ich let ĠProcess ĠEvent ĠM ixture ĠModel Ġ( DP EM M ) Ġin Ġwhich Ġthe Ġnumber Ġof Ġevents Ġcan Ġbe Ġlearned Ġautomatically Ġfrom Ġdata . ĠHowever , Ġboth ĠL EM Ġand ĠDP EM M Ġhave Ġtwo Ġlimitations : Ġ( 1 ) Ġthey Ġassume Ġthat Ġall Ġwords Ġin Ġa Ġdocument Ġare Ġgenerated Ġfrom Ġa Ġsingle Ġevent Ġwhich Ġcan Ġbe Ġrepresented Ġby Ġa Ġquadru ple ĠIN LINE FORM 0 Ġentity , Ġlocation , Ġkeyword , Ġdate ĠIN LINE FORM 1 Ġ. ĠHowever , Ġlong Ġtexts Ġsuch Ġnews Ġarticles Ġoften Ġdescribe Ġmultiple Ġevents Ġwhich Ġclearly Ġviolates Ġthis Ġassumption ; Ġ( 2 ) ĠDuring Ġthe Ġinference Ġprocess Ġof Ġboth Ġapproaches , Ġthe ĠGibbs Ġsam pler Ġneeds Ġto Ġcompute Ġthe Ġconditional Ġposterior Ġdistribution Ġand Ġassigns Ġan Ġevent Ġfor Ġeach Ġdocument . ĠThis Ġis Ġtime Ġconsuming Ġand Ġtakes Ġlong Ġtime Ġto Ġconverge . </s> To Ġdeal Ġwith Ġthese Ġlimitations , Ġin Ġthis Ġpaper , Ġwe Ġpropose Ġthe ĠAd vers arial - ne ural ĠEvent ĠModel Ġ( A EM ) Ġbased Ġon Ġadvers arial Ġtraining Ġfor Ġopen - domain Ġevent Ġextraction . ĠThe Ġprinciple Ġidea Ġis Ġto Ġuse Ġa Ġgenerator Ġnetwork Ġto Ġlearn Ġthe Ġprojection Ġfunction Ġbetween Ġthe Ġdocument - event Ġdistribution Ġand Ġfour Ġevent Ġrelated Ġword Ġdistributions Ġ( entity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution ). ĠInstead Ġof Ġproviding Ġan Ġanalytic Ġapproximation , ĠA EM Ġuses Ġa Ġdiscrim inator Ġnetwork Ġto Ġdiscriminate Ġbetween Ġthe Ġreconstructed Ġdocuments Ġfrom Ġlatent Ġevents Ġand Ġthe Ġoriginal Ġinput Ġdocuments . ĠThis Ġessentially Ġhelps Ġthe Ġgenerator Ġto Ġconstruct Ġa Ġmore Ġrealistic Ġdocument Ġfrom Ġa Ġrandom Ġnoise Ġdrawn Ġfrom Ġa ĠDir ich let Ġdistribution . ĠDue Ġto Ġthe Ġflexibility Ġof Ġneural Ġnetworks , Ġthe Ġgenerator Ġis Ġcapable Ġof Ġlearning Ġcomplicated Ġnon linear Ġdistributions . ĠAnd Ġthe Ġsupervision Ġsignal Ġprovided Ġby Ġthe Ġdiscrim inator Ġwill Ġhelp Ġgenerator Ġto Ġcapture Ġthe Ġevent - related Ġpatterns . ĠFurthermore , Ġthe Ġdiscrim inator Ġalso Ġprovides Ġlow - dimensional Ġdiscrim in ative Ġfeatures Ġwhich Ġcan Ġbe Ġused Ġto Ġvisualize Ġdocuments Ġand Ġevents . </s> The Ġmain Ġcontributions Ġof Ġthe Ġpaper Ġare Ġsummarized Ġbelow : </s> Related ĠWork </s> Our Ġwork Ġis Ġrelated Ġto Ġtwo Ġlines Ġof Ġresearch , Ġevent Ġextraction Ġand ĠGener ative ĠAd vers arial ĠNets . </s> Event ĠExt raction </s> Recently Ġthere Ġhas Ġbeen Ġmuch Ġinterest Ġin Ġevent Ġextraction Ġfrom Ġonline Ġtexts , Ġand Ġapproaches Ġcould Ġbe Ġcategorized Ġas Ġdomain - specific Ġand Ġopen - domain Ġevent Ġextraction . </s> Domain - specific Ġevent Ġextraction Ġoften Ġfocuses Ġon Ġthe Ġspecific Ġtypes Ġof Ġevents Ġ( e . g . Ġsports Ġevents Ġor Ġcity Ġevents ). ĠPan em Ġet Ġal . Ġpan em 2014 struct ured Ġdevised Ġa Ġnovel Ġalgorithm Ġto Ġextract Ġattribute - value Ġpairs Ġand Ġmapped Ġthem Ġto Ġmanually Ġgenerated Ġschemes Ġfor Ġextracting Ġthe Ġnatural Ġdisaster Ġevents . ĠSimilarly , Ġto Ġextract Ġthe Ġcity - tra ffic Ġrelated Ġevent , ĠAn anth aram Ġet Ġal . Ġan anth aram 2015 ext ract ing Ġviewed Ġthe Ġtask Ġas Ġa Ġsequential Ġtagging Ġproblem Ġand Ġproposed Ġan Ġapproach Ġbased Ġon Ġthe Ġconditional Ġrandom Ġfields . ĠZhang Ġz hang 2018 event Ġproposed Ġan Ġevent Ġextraction Ġapproach Ġbased Ġon Ġimitation Ġlearning , Ġespecially Ġon Ġinverse Ġreinforcement Ġlearning . </s> Open - domain Ġevent Ġextraction Ġaims Ġto Ġextract Ġevents Ġwithout Ġlimiting Ġthe Ġspecific Ġtypes Ġof Ġevents . ĠTo Ġanalyze Ġindividual Ġmessages Ġand Ġinduce Ġa Ġcanonical Ġvalue Ġfor Ġeach Ġevent , ĠBenson Ġet Ġal . Ġb enson 2011 event Ġproposed Ġan Ġapproach Ġbased Ġon Ġa Ġstructured Ġgraphical Ġmodel . ĠBy Ġrepresenting Ġan Ġevent Ġwith Ġa Ġbinary Ġtuple Ġwhich Ġis Ġconstituted Ġby Ġa Ġnamed Ġentity Ġand Ġa Ġdate , ĠR itter Ġet Ġal . Ġr itter 2012 open Ġemployed Ġsome Ġstatistic Ġto Ġmeasure Ġthe Ġstrength Ġof Ġassociations Ġbetween Ġa Ġnamed Ġentity Ġand Ġa Ġdate . ĠThe Ġproposed Ġsystem Ġrelies Ġon Ġa Ġsupervised Ġlabel er Ġtrained Ġon Ġannot ated Ġdata . ĠIn ĠB IB REF 1 Ġ, ĠAbdel ha q Ġet Ġal . Ġdeveloped Ġa Ġreal - time Ġevent Ġextraction Ġsystem Ġcalled ĠEven Tweet , Ġand Ġeach Ġevent Ġis Ġrepresented Ġas Ġa Ġtriple Ġconstituted Ġby Ġtime , Ġlocation Ġand Ġkeywords . ĠTo Ġextract Ġmore Ġinformation , ĠWang Ġel Ġal . Ġw ang 2015 see ft Ġdeveloped Ġa Ġsystem Ġemploying Ġthe Ġlinks Ġin Ġtweets Ġand Ġcomb ing Ġtweets Ġwith Ġlinked Ġarticles Ġto Ġidentify Ġevents . ĠXia Ġel Ġal . Ġx ia 2015 new Ġcombined Ġtexts Ġwith Ġthe Ġlocation Ġinformation Ġto Ġdetect Ġthe Ġevents Ġwith Ġlow Ġspatial Ġand Ġtemporal Ġdeviations . ĠZhou Ġet Ġal . Ġz hou 2014 simple , zhou 2017 event Ġrepresented Ġevent Ġas Ġa Ġquadru ple Ġand Ġproposed Ġtwo ĠBay esian Ġmodels Ġto Ġextract Ġevents Ġfrom Ġtweets . </s> Gener ative ĠAd vers arial ĠNets </s> As Ġa Ġneural - based Ġgener ative Ġmodel , ĠGener ative ĠAd vers arial ĠNets ĠB IB REF 3 Ġhave Ġbeen Ġextensively Ġresearched Ġin Ġnatural Ġlanguage Ġprocessing Ġ( N LP ) Ġcommunity . </s> For Ġtext Ġgeneration , Ġthe Ġsequence Ġgener ative Ġadvers arial Ġnetwork Ġ( Se q GAN ) Ġproposed Ġin ĠB IB REF 4 Ġincorporated Ġa Ġpolicy Ġgradient Ġstrategy Ġto Ġoptimize Ġthe Ġgeneration Ġprocess . ĠBased Ġon Ġthe Ġpolicy Ġgradient , ĠLin Ġet Ġal . Ġlin 2017 ad vers arial Ġproposed ĠRank GAN Ġto Ġcapture Ġthe Ġrich Ġstructures Ġof Ġlanguage Ġby Ġranking Ġand Ġanalyzing Ġa Ġcollection Ġof Ġhuman - written Ġand Ġmachine - written Ġsentences . ĠTo Ġovercome Ġmode Ġcollapse Ġwhen Ġdealing Ġwith Ġdiscrete Ġdata , ĠFed us Ġet Ġal . Ġfed us 2018 mask gan Ġproposed ĠMask GAN Ġwhich Ġused Ġan Ġactor - crit ic Ġconditional ĠG AN Ġto Ġfill Ġin Ġmissing Ġtext Ġconditioned Ġon Ġthe Ġsurrounding Ġcontext . ĠAlong Ġthis Ġline , ĠWang Ġet Ġal . Ġw ang 2018 sent igan Ġproposed ĠSent i GAN Ġto Ġgenerate Ġtexts Ġof Ġdifferent Ġsentiment Ġlabels . ĠBesides , ĠLi Ġet Ġal . Ġli 2018 learning Ġimproved Ġthe Ġperformance Ġof Ġsemi - super vised Ġtext Ġclassification Ġusing Ġadvers arial Ġtraining , ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġdesigned ĠG AN - based Ġmodels Ġfor Ġdistance Ġsupervision Ġrelation Ġextraction . </s> Although Ġvarious ĠG AN Ġbased Ġapproaches Ġhave Ġbeen Ġexplored Ġfor Ġmany Ġapplications , Ġnone Ġof Ġthese Ġapproaches Ġtackles Ġopen - domain Ġevent Ġextraction Ġfrom Ġonline Ġtexts . ĠWe Ġpropose Ġa Ġnovel ĠG AN - based Ġevent Ġextraction Ġmodel Ġcalled ĠA EM . ĠCompared Ġwith Ġthe Ġprevious Ġmodels , ĠA EM Ġhas Ġthe Ġfollowing Ġdifferences : Ġ( 1 ) ĠUnlike Ġmost ĠG AN - based Ġtext Ġgeneration Ġapproaches , Ġa Ġgenerator Ġnetwork Ġis Ġemployed Ġin ĠA EM Ġto Ġlearn Ġthe Ġprojection Ġfunction Ġbetween Ġan Ġevent Ġdistribution Ġand Ġthe Ġevent - related Ġword Ġdistributions Ġ( entity , Ġlocation , Ġkeyword , Ġdate ). ĠThe Ġlearned Ġgenerator Ġcaptures Ġevent - related Ġpatterns Ġrather Ġthan Ġgenerating Ġtext Ġsequence ; Ġ( 2 ) ĠDifferent Ġfrom ĠL EM Ġand ĠDP EM M , ĠA EM Ġuses Ġa Ġgenerator Ġnetwork Ġto Ġcapture Ġthe Ġevent - related Ġpatterns Ġand Ġis Ġable Ġto Ġmine Ġevents Ġfrom Ġdifferent Ġtext Ġsources Ġ( short Ġand Ġlong ). ĠMoreover , Ġunlike Ġtraditional Ġinference Ġprocedure , Ġsuch Ġas ĠGibbs Ġsampling Ġused Ġin ĠL EM Ġand ĠDP EM M , ĠA EM Ġcould Ġextract Ġthe Ġevents Ġmore Ġefficiently Ġdue Ġto Ġthe ĠCU DA Ġacceleration ; Ġ( 3 ) ĠThe Ġdiscrim in ative Ġfeatures Ġlearned Ġby Ġthe Ġdiscrim inator Ġof ĠA EM Ġprovide Ġa Ġstraightforward Ġway Ġto Ġvisualize Ġthe Ġextracted Ġevents . </s> Method ology </s> We Ġdescribe ĠAd vers arial - ne ural ĠEvent ĠModel Ġ( A EM ) Ġin Ġthis Ġsection . ĠAn Ġevent Ġis Ġrepresented Ġas Ġa Ġquadru ple Ġ< ĠIN LINE FORM 0 Ġ> , Ġwhere ĠIN LINE FORM 1 Ġstands Ġfor Ġnon - location Ġnamed Ġentities , ĠIN LINE FORM 2 Ġfor Ġa Ġlocation , ĠIN LINE FORM 3 Ġfor Ġevent - related Ġkeywords , ĠIN LINE FORM 4 Ġfor Ġa Ġdate , Ġand Ġeach Ġcomponent Ġin Ġthe Ġtuple Ġis Ġrepresented Ġby Ġcomponent - specific Ġrepresentative Ġwords . </s> A EM Ġis Ġconstituted Ġby Ġthree Ġcomponents : Ġ( 1 ) ĠThe Ġdocument Ġrepresentation Ġmodule , Ġas Ġshown Ġat Ġthe Ġtop Ġof ĠFigure ĠFIG REF 4 Ġ, Ġdefines Ġa Ġdocument Ġrepresentation Ġapproach Ġwhich Ġconverts Ġan Ġinput Ġdocument Ġfrom Ġthe Ġonline Ġtext Ġcorpus Ġinto ĠIN LINE FORM 0 Ġwhich Ġcaptures Ġthe Ġkey Ġevent Ġelements ; Ġ( 2 ) ĠThe Ġgenerator ĠIN LINE FORM 1 Ġ, Ġas Ġshown Ġin Ġthe Ġlower - left Ġpart Ġof ĠFigure ĠFIG REF 4 Ġ, Ġgenerates Ġa Ġfake Ġdocument ĠIN LINE FORM 2 Ġwhich Ġis Ġconstituted Ġby Ġfour Ġmult in omial Ġdistributions Ġusing Ġan Ġevent Ġdistribution ĠIN LINE FORM 3 Ġdrawn Ġfrom Ġa ĠDir ich let Ġdistribution Ġas Ġinput ; Ġ( 3 ) ĠThe Ġdiscrim inator ĠIN LINE FORM 4 Ġ, Ġas Ġshown Ġin Ġthe Ġlower - right Ġpart Ġof ĠFigure ĠFIG REF 4 Ġ, Ġdistinguishes Ġthe Ġreal Ġdocuments Ġfrom Ġthe Ġfake Ġones Ġand Ġits Ġoutput Ġis Ġsubsequently Ġemployed Ġas Ġa Ġlearning Ġsignal Ġto Ġupdate Ġthe ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġ. ĠThe Ġdetails Ġof Ġeach Ġcomponent Ġare Ġpresented Ġbelow . </s> Document ĠRepresent ation </s> Each Ġdocument ĠIN LINE FORM 0 Ġin Ġa Ġgiven Ġcorpus ĠIN LINE FORM 1 Ġis Ġrepresented Ġas Ġa Ġconc aten ation Ġof Ġ4 Ġmult in omial Ġdistributions Ġwhich Ġare Ġentity Ġdistribution Ġ( ĠIN LINE FORM 2 Ġ), Ġlocation Ġdistribution Ġ( ĠIN LINE FORM 3 Ġ), Ġkeyword Ġdistribution Ġ( ĠIN LINE FORM 4 Ġ) Ġand Ġdate Ġdistribution Ġ( ĠIN LINE FORM 5 Ġ) Ġof Ġthe Ġdocument . ĠAs Ġfour Ġdistributions Ġare Ġcalculated Ġin Ġa Ġsimilar Ġway , Ġwe Ġonly Ġdescribe Ġthe Ġcomputation Ġof Ġthe Ġentity Ġdistribution Ġbelow Ġas Ġan Ġexample . </s> The Ġentity Ġdistribution ĠIN LINE FORM 0 Ġis Ġrepresented Ġby Ġa Ġnormalized ĠIN LINE FORM 1 Ġ- dimensional Ġvector Ġweighted Ġby ĠTF - ID F , Ġand Ġit Ġis Ġcalculated Ġas : ĠIN LINE FORM 2 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġpseudo Ġcorpus Ġconstructed Ġby Ġremoving Ġall Ġnon - entity Ġwords Ġfrom ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġis Ġthe Ġtotal Ġnumber Ġof Ġdistinct Ġentities Ġin Ġa Ġcorpus , ĠIN LINE FORM 3 Ġdenotes Ġthe Ġnumber Ġof ĠIN LINE FORM 4 Ġ- th Ġentity Ġappeared Ġin Ġdocument ĠIN LINE FORM 5 Ġ, ĠIN LINE FORM 6 Ġrepresents Ġthe Ġnumber Ġof Ġdocuments Ġin Ġthe Ġcorpus , Ġand ĠIN LINE FORM 7 Ġis Ġthe Ġnumber Ġof Ġdocuments Ġthat Ġcontain ĠIN LINE FORM 8 Ġ- th Ġentity , Ġand Ġthe Ġobtained ĠIN LINE FORM 9 Ġdenotes Ġthe Ġrelevance Ġbetween ĠIN LINE FORM 10 Ġ- th Ġentity Ġand Ġdocument ĠIN LINE FORM 11 Ġ. </s> Similarly , Ġlocation Ġdistribution ĠIN LINE FORM 0 Ġ, Ġkeyword Ġdistribution ĠIN LINE FORM 1 Ġand Ġdate Ġdistribution ĠIN LINE FORM 2 Ġof ĠIN LINE FORM 3 Ġcould Ġbe Ġcalculated Ġin Ġthe Ġsame Ġway , Ġand Ġthe Ġdimensions Ġof Ġthese Ġdistributions Ġare Ġden oted Ġas ĠIN LINE FORM 4 Ġ, ĠIN LINE FORM 5 Ġand ĠIN LINE FORM 6 Ġ, Ġrespectively . ĠFinally , Ġeach Ġdocument ĠIN LINE FORM 7 Ġin Ġthe Ġcorpus Ġis Ġrepresented Ġby Ġa ĠIN LINE FORM 8 Ġ- dimensional Ġ( ĠIN LINE FORM 9 Ġ= ĠIN LINE FORM 10 Ġ+ ĠIN LINE FORM 11 Ġ+ ĠIN LINE FORM 12 Ġ+ ĠIN LINE FORM 13 Ġ) Ġvector ĠIN LINE FORM 14 Ġby Ġconc aten ating Ġfour Ġcomputed Ġdistributions . </s> Network ĠArchitecture </s> The Ġgenerator Ġnetwork ĠIN LINE FORM 0 Ġis Ġdesigned Ġto Ġlearn Ġthe Ġprojection Ġfunction Ġbetween Ġthe Ġdocument - event Ġdistribution ĠIN LINE FORM 1 Ġand Ġthe Ġfour Ġdocument - level Ġword Ġdistributions Ġ( entity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution ). </s> More Ġconcrete ly , ĠIN LINE FORM 0 Ġconsists Ġof Ġa ĠIN LINE FORM 1 Ġ- dimensional Ġdocument - event Ġdistribution Ġlayer , ĠIN LINE FORM 2 Ġ- dimensional Ġhidden Ġlayer Ġand ĠIN LINE FORM 3 Ġ- dimensional Ġevent - related Ġword Ġdistribution Ġlayer . ĠHere , ĠIN LINE FORM 4 Ġdenotes Ġthe Ġevent Ġnumber , ĠIN LINE FORM 5 Ġis Ġthe Ġnumber Ġof Ġunits Ġin Ġthe Ġhidden Ġlayer , ĠIN LINE FORM 6 Ġis Ġthe Ġvocabulary Ġsize Ġand Ġequals Ġto ĠIN LINE FORM 7 Ġ+ ĠIN LINE FORM 8 Ġ+ ĠIN LINE FORM 9 Ġ+ ĠIN LINE FORM 10 Ġ. ĠAs Ġshown Ġin ĠFigure ĠFIG REF 4 Ġ, ĠIN LINE FORM 11 Ġfirst ly Ġemploys Ġa Ġrandom Ġdocument - event Ġdistribution ĠIN LINE FORM 12 Ġas Ġan Ġinput . ĠTo Ġmodel Ġthe Ġmult in omial Ġproperty Ġof Ġthe Ġdocument - event Ġdistribution , ĠIN LINE FORM 13 Ġis Ġdrawn Ġfrom Ġa ĠDir ich let Ġdistribution Ġparameter ized Ġwith ĠIN LINE FORM 14 Ġwhich Ġis Ġformulated Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġhyper - param eter Ġof Ġthe Ġdir ich let Ġdistribution , ĠIN LINE FORM 1 Ġis Ġthe Ġnumber Ġof Ġevents Ġwhich Ġshould Ġbe Ġset Ġin ĠA EM , ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġrepresents Ġthe Ġproportion Ġof Ġevent ĠIN LINE FORM 4 Ġin Ġthe Ġdocument Ġand ĠIN LINE FORM 5 Ġ. </s> Sub sequently , ĠIN LINE FORM 0 Ġtransforms ĠIN LINE FORM 1 Ġinto Ġa ĠIN LINE FORM 2 Ġ- dimensional Ġhidden Ġspace Ġusing Ġa Ġlinear Ġlayer Ġfollowed Ġby Ġlayer Ġnormal ization , Ġand Ġthe Ġtransformation Ġis Ġdefined Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġrepresents Ġthe Ġweight Ġmatrix Ġof Ġhidden Ġlayer , Ġand ĠIN LINE FORM 1 Ġdenotes Ġthe Ġbias Ġterm , ĠIN LINE FORM 2 Ġis Ġthe Ġparameter Ġof ĠLe aky Re LU Ġactivation Ġand Ġis Ġset Ġto Ġ0 . 1 , ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġdenote Ġthe Ġnormalized Ġhidden Ġstates Ġand Ġthe Ġoutputs Ġof Ġthe Ġhidden Ġlayer , Ġand ĠIN LINE FORM 5 Ġrepresents Ġthe Ġlayer Ġnormal ization . </s> Then , Ġto Ġproject ĠIN LINE FORM 0 Ġinto Ġfour Ġdocument - level Ġevent Ġrelated Ġword Ġdistributions Ġ( ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġshown Ġin ĠFigure ĠFIG REF 4 Ġ), Ġfour Ġsub nets Ġ( each Ġcontains Ġa Ġlinear Ġlayer , Ġa Ġbatch Ġnormal ization Ġlayer Ġand Ġa Ġsoft max Ġlayer ) Ġare Ġemployed Ġin ĠIN LINE FORM 5 Ġ. ĠAnd Ġthe Ġexact Ġtransformation Ġis Ġbased Ġon Ġthe Ġformulas Ġbelow : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġmeans Ġsoft max Ġlayer , ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġand ĠIN LINE FORM 4 Ġdenote Ġthe Ġweight Ġmat rices Ġof Ġthe Ġlinear Ġlayers Ġin Ġsub nets , ĠIN LINE FORM 5 Ġ, ĠIN LINE FORM 6 Ġ, ĠIN LINE FORM 7 Ġand ĠIN LINE FORM 8 Ġrepresent Ġthe Ġcorresponding Ġbias Ġterms , ĠIN LINE FORM 9 Ġ, ĠIN LINE FORM 10 Ġ, ĠIN LINE FORM 11 Ġand ĠIN LINE FORM 12 Ġare Ġstate Ġvectors . ĠIN LINE FORM 13 Ġ, ĠIN LINE FORM 14 Ġ, ĠIN LINE FORM 15 Ġand ĠIN LINE FORM 16 Ġdenote Ġthe Ġgenerated Ġentity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution , Ġrespectively , Ġthat Ġcorrespond Ġto Ġthe Ġgiven Ġevent Ġdistribution ĠIN LINE FORM 17 Ġ. ĠAnd Ġeach Ġdimension Ġrepresents Ġthe Ġrelevance Ġbetween Ġcorresponding Ġentity / location / key word / date Ġterm Ġand Ġthe Ġinput Ġevent Ġdistribution . </s> Finally , Ġfour Ġgenerated Ġdistributions Ġare Ġconc aten ated Ġto Ġrepresent Ġthe Ġgenerated Ġdocument ĠIN LINE FORM 0 Ġcorresponding Ġto Ġthe Ġinput ĠIN LINE FORM 1 Ġ: ĠDIS PLAY FORM 0 </s> The Ġdiscrim inator Ġnetwork ĠIN LINE FORM 0 Ġis Ġdesigned Ġas Ġa Ġfully - connected Ġnetwork Ġwhich Ġcontains Ġan Ġinput Ġlayer , Ġa Ġdiscrim in ative Ġfeature Ġlayer Ġ( disc rim in ative Ġfeatures Ġare Ġemployed Ġfor Ġevent Ġvisualization ) Ġand Ġan Ġoutput Ġlayer . ĠIn ĠA EM , ĠIN LINE FORM 1 Ġuses Ġfake Ġdocument ĠIN LINE FORM 2 Ġand Ġreal Ġdocument ĠIN LINE FORM 3 Ġas Ġinput Ġand Ġoutputs Ġthe Ġsignal ĠIN LINE FORM 4 Ġto Ġindicate Ġthe Ġsource Ġof Ġthe Ġinput Ġdata Ġ( lower Ġvalue Ġdenotes Ġthat ĠIN LINE FORM 5 Ġis Ġprone Ġto Ġpredict Ġthe Ġinput Ġdata Ġas Ġa Ġfake Ġdocument Ġand Ġvice Ġversa ). </s> As Ġhave Ġpreviously Ġbeen Ġdiscussed Ġin ĠB IB REF 7 Ġ, ĠB IB REF 8 Ġ, Ġlips ch itz Ġcontinuity Ġof ĠIN LINE FORM 0 Ġnetwork Ġis Ġcrucial Ġto Ġthe Ġtraining Ġof Ġthe ĠG AN - based Ġapproaches . ĠTo Ġensure Ġthe Ġlips ch itz Ġcontinuity Ġof ĠIN LINE FORM 1 Ġ, Ġwe Ġemploy Ġthe Ġspectral Ġnormal ization Ġtechnique ĠB IB REF 9 Ġ. ĠMore Ġconcrete ly , Ġfor Ġeach Ġlinear Ġlayer ĠIN LINE FORM 2 Ġ( b ias Ġterm Ġis Ġomitted Ġfor Ġsimplicity ) Ġin ĠIN LINE FORM 3 Ġ, Ġthe Ġweight Ġmatrix ĠIN LINE FORM 4 Ġis Ġnormalized Ġby ĠIN LINE FORM 5 Ġ. ĠHere , ĠIN LINE FORM 6 Ġis Ġthe Ġspectral Ġnorm Ġof Ġthe Ġweight Ġmatrix ĠIN LINE FORM 7 Ġwith Ġthe Ġdefinition Ġbelow : ĠDIS PLAY FORM 0 </s> which Ġis Ġequivalent Ġto Ġthe Ġlargest Ġsingular Ġvalue Ġof ĠIN LINE FORM 0 Ġ. ĠThe Ġweight Ġmatrix ĠIN LINE FORM 1 Ġis Ġthen Ġnormalized Ġusing : ĠDIS PLAY FORM 0 </s> Obviously , Ġthe Ġnormalized Ġweight Ġmatrix ĠIN LINE FORM 0 Ġsatisfies Ġthat ĠIN LINE FORM 1 Ġand Ġfurther Ġensures Ġthe Ġlips ch itz Ġcontinuity Ġof Ġthe ĠIN LINE FORM 2 Ġnetwork ĠB IB REF 9 Ġ. ĠTo Ġreduce Ġthe Ġhigh Ġcost Ġof Ġcomputing Ġspectral Ġnorm ĠIN LINE FORM 3 Ġusing Ġsingular Ġvalue Ġdecom position Ġat Ġeach Ġiteration , Ġwe Ġfollow ĠB IB REF 10 Ġand Ġemploy Ġthe Ġpower Ġiteration Ġmethod Ġto Ġestimate ĠIN LINE FORM 4 Ġinstead . ĠWith Ġthis Ġsubstitution , Ġthe Ġspectral Ġnorm Ġcan Ġbe Ġestimated Ġwith Ġvery Ġsmall Ġadditional Ġcomputational Ġtime . </s> Object ive Ġand ĠTraining ĠProcedure </s> The Ġreal Ġdocument ĠIN LINE FORM 0 Ġand Ġfake Ġdocument ĠIN LINE FORM 1 Ġshown Ġin ĠFigure ĠFIG REF 4 Ġcould Ġbe Ġviewed Ġas Ġrandom Ġsamples Ġfrom Ġtwo Ġdistributions ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġ, Ġand Ġeach Ġof Ġthem Ġis Ġa Ġjoint Ġdistribution Ġconstituted Ġby Ġfour ĠDir ich let Ġdistributions Ġ( cor respond ing Ġto Ġentity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution ). ĠThe Ġtraining Ġobjective Ġof ĠA EM Ġis Ġto Ġlet Ġthe Ġdistribution ĠIN LINE FORM 4 Ġ( produced Ġby ĠIN LINE FORM 5 Ġnetwork ) Ġto Ġapproximate Ġthe Ġreal Ġdata Ġdistribution ĠIN LINE FORM 6 Ġas Ġmuch Ġas Ġpossible . </s> To Ġcompare Ġthe Ġdifferent ĠG AN Ġlosses , ĠKur ach Ġk ur ach 2018 gan Ġtakes Ġa Ġsober Ġview Ġof Ġthe Ġcurrent Ġstate Ġof ĠG AN Ġand Ġsuggests Ġthat Ġthe ĠJ ansen - Sh annon Ġdivergence Ġused Ġin ĠB IB REF 3 Ġperforms Ġmore Ġstable Ġthan Ġvariant Ġobjectives . ĠBesides , ĠKur ach Ġalso Ġadvocates Ġthat Ġthe Ġgradient Ġpenalty Ġ( GP ) Ġregular ization Ġdevised Ġin ĠB IB REF 8 Ġwill Ġfurther Ġimprove Ġthe Ġstability Ġof Ġthe Ġmodel . ĠThus , Ġthe Ġobjective Ġfunction Ġof Ġthe Ġproposed ĠA EM Ġis Ġdefined Ġas : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġdenotes Ġthe Ġdiscrim inator Ġloss , ĠIN LINE FORM 1 Ġrepresents Ġthe Ġgradient Ġpenalty Ġregular ization Ġloss , ĠIN LINE FORM 2 Ġis Ġthe Ġgradient Ġpenalty Ġcoefficient Ġwhich Ġtrade - off Ġthe Ġtwo Ġcomponents Ġof Ġobjective , ĠIN LINE FORM 3 Ġcould Ġbe Ġobtained Ġby Ġsampling Ġuniformly Ġalong Ġa Ġstraight Ġline Ġbetween ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġ, ĠIN LINE FORM 6 Ġdenotes Ġthe Ġcorresponding Ġdistribution . </s> The Ġtraining Ġprocedure Ġof ĠA EM Ġis Ġpresented Ġin ĠAl gorithm ĠSEC REF 15 Ġ, Ġwhere ĠIN LINE FORM 0 Ġis Ġthe Ġevent Ġnumber , ĠIN LINE FORM 1 Ġdenotes Ġthe Ġnumber Ġof Ġdiscrim inator Ġiterations Ġper Ġgenerator Ġiteration , ĠIN LINE FORM 2 Ġis Ġthe Ġbatch Ġsize , ĠIN LINE FORM 3 Ġrepresents Ġthe Ġlearning Ġrate , ĠIN LINE FORM 4 Ġand ĠIN LINE FORM 5 Ġare Ġhyper - param eters Ġof ĠAdam ĠB IB REF 11 Ġ, ĠIN LINE FORM 6 Ġdenotes ĠIN LINE FORM 7 Ġ. ĠIn Ġthis Ġpaper , Ġwe Ġset ĠIN LINE FORM 8 Ġ, ĠIN LINE FORM 9 Ġ, ĠIN LINE FORM 10 Ġ. ĠMoreover , ĠIN LINE FORM 11 Ġ, ĠIN LINE FORM 12 Ġand ĠIN LINE FORM 13 Ġare Ġset Ġas Ġ0 . 0002 , Ġ0 . 5 Ġand Ġ0 . 999 . </s> [ ! h ] ĠTraining Ġprocedure Ġfor ĠA EM Ġ[ 1 ] ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġ, ĠIN LINE FORM 4 Ġ, ĠIN LINE FORM 5 Ġ, ĠIN LINE FORM 6 Ġthe Ġtrained ĠIN LINE FORM 7 Ġand ĠIN LINE FORM 8 Ġ. ĠInitial ĠIN LINE FORM 9 Ġparameters ĠIN LINE FORM 10 Ġand ĠIN LINE FORM 11 Ġparameter ĠIN LINE FORM 12 ĠIN LINE FORM 13 Ġhas Ġnot Ġconver ged ĠIN LINE FORM 14 ĠIN LINE FORM 15 ĠSample ĠIN LINE FORM 16 Ġ, ĠSample Ġa Ġrandom ĠIN LINE FORM 17 ĠSample Ġa Ġrandom Ġnumber ĠIN LINE FORM 18 ĠIN LINE FORM 19 ĠIN LINE FORM 20 ĠIN LINE FORM 21 ĠIN LINE FORM 22 ĠIN LINE FORM 23 ĠIN LINE FORM 24 ĠSample ĠIN LINE FORM 25 Ġnoise ĠIN LINE FORM 26 ĠIN LINE FORM 27 </s> Event ĠGeneration </s> After Ġthe Ġmodel Ġtraining , Ġthe Ġgenerator ĠIN LINE FORM 0 Ġlearns Ġthe Ġmapping Ġfunction Ġbetween Ġthe Ġdocument - event Ġdistribution Ġand Ġthe Ġdocument - level Ġevent - related Ġword Ġdistributions Ġ( entity , Ġlocation , Ġkeyword Ġand Ġdate ). ĠIn Ġother Ġwords , Ġwith Ġan Ġevent Ġdistribution ĠIN LINE FORM 1 Ġas Ġinput , ĠIN LINE FORM 2 Ġcould Ġgenerate Ġthe Ġcorresponding Ġentity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution . </s> In ĠA EM , Ġwe Ġemploy Ġevent Ġseed ĠIN LINE FORM 0 Ġ, Ġan ĠIN LINE FORM 1 Ġ- dimensional Ġvector Ġwith Ġone - hot Ġencoding , Ġto Ġgenerate Ġthe Ġevent Ġrelated Ġword Ġdistributions . ĠFor Ġexample , Ġin Ġten Ġevent Ġsetting , ĠIN LINE FORM 2 Ġrepresents Ġthe Ġevent Ġseed Ġof Ġthe Ġfirst Ġevent . ĠWith Ġthe Ġevent Ġseed ĠIN LINE FORM 3 Ġas Ġinput , Ġthe Ġcorresponding Ġdistributions Ġcould Ġbe Ġgenerated Ġby ĠIN LINE FORM 4 Ġbased Ġon Ġthe Ġequation Ġbelow : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġ, ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġdenote Ġthe Ġentity Ġdistribution , Ġlocation Ġdistribution , Ġkeyword Ġdistribution Ġand Ġdate Ġdistribution Ġof Ġthe Ġfirst Ġevent Ġrespectively . </s> Exper iments </s> In Ġthis Ġsection , Ġwe Ġfirst ly Ġdescribe Ġthe Ġdatasets Ġand Ġbaseline Ġapproaches Ġused Ġin Ġour Ġexperiments Ġand Ġthen Ġpresent Ġthe Ġexperimental Ġresults . </s> Exper imental ĠSetup </s> To Ġvalidate Ġthe Ġeffectiveness Ġof ĠA EM Ġfor Ġextracting Ġevents Ġfrom Ġsocial Ġmedia Ġ( e . g . ĠTwitter ) Ġand Ġnews Ġmedia Ġsites Ġ( e . g . ĠGoogle Ġnews ), Ġthree Ġdatasets Ġ( F SD ĠB IB REF 12 Ġ, ĠTwitter , Ġand ĠGoogle Ġdatasets ) Ġare Ġemployed . ĠDetails Ġare Ġsummarized Ġbelow : </s> F SD Ġdataset Ġ( social Ġmedia ) Ġis Ġthe Ġfirst Ġstory Ġdetection Ġdataset Ġcontaining Ġ2 , 499 Ġtweets . ĠWe Ġfilter Ġout Ġevents Ġmentioned Ġin Ġless Ġthan Ġ15 Ġtweets Ġsince Ġevents Ġmentioned Ġin Ġvery Ġfew Ġtweets Ġare Ġless Ġlikely Ġto Ġbe Ġsignificant . ĠThe Ġfinal Ġdataset Ġcontains Ġ2 , 453 Ġtweets Ġannot ated Ġwith Ġ20 Ġevents . </s> Twitter Ġdataset Ġ( social Ġmedia ) Ġis Ġcollected Ġfrom Ġtweets Ġpublished Ġin Ġthe Ġmonth Ġof ĠDecember Ġin Ġ2010 Ġusing ĠTwitter Ġstreaming ĠAPI . ĠIt Ġcontains Ġ1 , 000 Ġtweets Ġannot ated Ġwith Ġ20 Ġevents . </s> Google Ġdataset Ġ( news Ġarticle ) Ġis Ġa Ġsubset Ġof ĠGD EL T ĠEvent ĠDatabase ĠIN LINE FORM 0 Ġ, Ġdocuments Ġare Ġretrieved Ġby Ġevent Ġrelated Ġwords . ĠFor Ġexample , Ġdocuments Ġwhich Ġcontain Ġ` mal ays ia ', Ġ` air line ', Ġ` search ' Ġand Ġ` plane ' Ġare Ġretrieved Ġfor Ġevent ĠMH 370 . ĠBy Ġcombining Ġ30 Ġevents Ġrelated Ġdocuments , Ġthe Ġdataset Ġcontains Ġ11 , 909 Ġnews Ġarticles . </s> We Ġchoose Ġthe Ġfollowing Ġthree Ġmodels Ġas Ġthe Ġbas elines : </s> K - me ans Ġis Ġa Ġwell Ġknown Ġdata Ġclust ering Ġalgorithm , Ġwe Ġimplement Ġthe Ġalgorithm Ġusing Ġsk learn Ġtool box , Ġand Ġrepresent Ġdocuments Ġusing Ġbag - of - words Ġweighted Ġby ĠTF - ID F . </s> LE M ĠB IB REF 13 Ġis Ġa ĠBay esian Ġmodeling Ġapproach Ġfor Ġopen - domain Ġevent Ġextraction . ĠIt Ġtreats Ġan Ġevent Ġas Ġa Ġlatent Ġvariable Ġand Ġmodels Ġthe Ġgeneration Ġof Ġan Ġevent Ġas Ġa Ġjoint Ġdistribution Ġof Ġits Ġindividual Ġevent Ġelements . ĠWe Ġimplement Ġthe Ġalgorithm Ġwith Ġthe Ġdefault Ġconfiguration . </s> DP EM M ĠB IB REF 14 Ġis Ġa Ġnon - param etric Ġmixture Ġmodel Ġfor Ġevent Ġextraction . ĠIt Ġaddresses Ġthe Ġlimitation Ġof ĠL EM Ġthat Ġthe Ġnumber Ġof Ġevents Ġshould Ġbe Ġknown Ġbeforehand . ĠWe Ġimplement Ġthe Ġmodel Ġwith Ġthe Ġdefault Ġconfiguration . </s> For Ġsocial Ġmedia Ġtext Ġcorpus Ġ( F SD Ġand ĠTwitter ), Ġa Ġnamed Ġentity Ġtag ger Ġspecifically Ġbuilt Ġfor ĠTwitter Ġis Ġused Ġto Ġextract Ġnamed Ġentities Ġincluding Ġlocations Ġfrom Ġtweets . ĠA ĠTwitter ĠPart - of - Spe ech Ġ( POS ) Ġtag ger ĠB IB REF 15 Ġis Ġused Ġfor ĠPOS Ġtagging Ġand Ġonly Ġwords Ġtagged Ġwith Ġnoun s , Ġverbs Ġand Ġadject ives Ġare Ġretained Ġas Ġkeywords . ĠFor Ġthe ĠGoogle Ġdataset , Ġwe Ġuse Ġthe ĠStanford ĠNamed ĠEntity ĠRecogn izer Ġto Ġidentify Ġthe Ġnamed Ġentities Ġ( organ ization , Ġlocation Ġand Ġperson ). ĠDue Ġto Ġthe Ġ` date ' Ġinformation Ġnot Ġbeing Ġprovided Ġin Ġthe ĠGoogle Ġdataset , Ġwe Ġfurther Ġdivide Ġthe Ġnon - location Ġnamed Ġentities Ġinto Ġtwo Ġcategories Ġ( ` person ' Ġand Ġ` organ ization ') Ġand Ġemploy Ġa Ġquadru ple Ġ< organ ization , Ġlocation , Ġperson , Ġkeyword > Ġto Ġdenote Ġan Ġevent Ġin Ġnews Ġarticles . ĠWe Ġalso Ġremove Ġcommon Ġstop words Ġand Ġonly Ġkeep Ġthe Ġrecognized Ġnamed Ġentities Ġand Ġthe Ġtokens Ġwhich Ġare Ġverbs , Ġnoun s Ġor Ġadject ives . </s> Exper imental ĠResults </s> To Ġevaluate Ġthe Ġperformance Ġof Ġthe Ġproposed Ġapproach , Ġwe Ġuse Ġthe Ġevaluation Ġmetrics Ġsuch Ġas Ġprecision , Ġrecall Ġand ĠF - me asure . ĠPrecision Ġis Ġdefined Ġas Ġthe Ġproportion Ġof Ġthe Ġcorrectly Ġidentified Ġevents Ġout Ġof Ġthe Ġmodel Ġgenerated Ġevents . ĠRecall Ġis Ġdefined Ġas Ġthe Ġproportion Ġof Ġcorrectly Ġidentified Ġtrue Ġevents . ĠFor Ġcalculating Ġthe Ġprecision Ġof Ġthe Ġ4 - t uple , Ġwe Ġuse Ġfollowing Ġcriteria : </s> ( 1 ) ĠDo Ġthe Ġentity / organ ization , Ġlocation , Ġdate / person Ġand Ġkeyword Ġthat Ġwe Ġhave Ġextracted Ġrefer Ġto Ġthe Ġsame Ġevent ? </s> ( 2 ) ĠIf Ġthe Ġextracted Ġrepresentation Ġcontains Ġkeywords , Ġare Ġthey Ġinformative Ġenough Ġto Ġtell Ġus Ġwhat Ġhappened ? </s> Table ĠT AB REF 35 Ġshows Ġthe Ġevent Ġextraction Ġresults Ġon Ġthe Ġthree Ġdatasets . ĠThe Ġstatistics Ġare Ġobtained Ġwith Ġthe Ġdefault Ġparameter Ġsetting Ġthat ĠIN LINE FORM 0 Ġis Ġset Ġto Ġ5 , Ġnumber Ġof Ġhidden Ġunits ĠIN LINE FORM 1 Ġis Ġset Ġto Ġ200 , Ġand ĠIN LINE FORM 2 Ġcontains Ġthree Ġfully - connected Ġlayers . ĠThe Ġevent Ġnumber ĠIN LINE FORM 3 Ġfor Ġthree Ġdatasets Ġare Ġset Ġto Ġ25 , Ġ25 Ġand Ġ35 , Ġrespectively . ĠThe Ġexamples Ġof Ġextracted Ġevents Ġare Ġshown Ġin ĠTable . ĠT AB REF 36 Ġ. </s> It Ġcan Ġbe Ġobserved Ġthat ĠK - me ans Ġperforms Ġthe Ġworst Ġover Ġall Ġthree Ġdatasets . ĠOn Ġthe Ġsocial Ġmedia Ġdatasets , ĠA EM Ġout po er forms Ġboth ĠL EM Ġand ĠDP EM M Ġby Ġ6 . 5 % Ġand Ġ1 . 7 % Ġrespectively Ġin ĠF - me asure Ġon Ġthe ĠF SD Ġdataset , Ġand Ġ4 . 4 % Ġand Ġ3 . 7 % Ġin ĠF - me asure Ġon Ġthe ĠTwitter Ġdataset . ĠWe Ġcan Ġalso Ġobserve Ġthat Ġapart Ġfrom ĠK - me ans , Ġall Ġthe Ġapproaches Ġperform Ġworse Ġon Ġthe ĠTwitter Ġdataset Ġcompared Ġto ĠF SD , Ġpossibly Ġdue Ġto Ġthe Ġlimited Ġsize Ġof Ġthe ĠTwitter Ġdataset . ĠMoreover , Ġon Ġthe ĠGoogle Ġdataset , Ġthe Ġproposed ĠA EM Ġperforms Ġsignificantly Ġbetter Ġthan ĠL EM Ġand ĠDP EM M . ĠIt Ġimproves Ġupon ĠL EM Ġby Ġ15 . 5 % Ġand Ġupon ĠDP EM M Ġby Ġmore Ġthan Ġ30 % Ġin ĠF - me asure . ĠThis Ġis Ġbecause : Ġ( 1 ) Ġthe Ġassumption Ġmade Ġby ĠL EM Ġand ĠDP EM M Ġthat Ġall Ġwords Ġin Ġa Ġdocument Ġare Ġgenerated Ġfrom Ġa Ġsingle Ġevent Ġis Ġnot Ġsuitable Ġfor Ġlong Ġtext Ġsuch Ġas Ġnews Ġarticles ; Ġ( 2 ) ĠDP EM M Ġgenerates Ġtoo Ġmany Ġirrelevant Ġevents Ġwhich Ġleads Ġto Ġa Ġvery Ġlow Ġprecision Ġscore . ĠOverall , Ġwe Ġsee Ġthe Ġsuperior Ġperformance Ġof ĠA EM Ġacross Ġall Ġdatasets , Ġwith Ġmore Ġsignificant Ġimprovement Ġon Ġthe Ġfor ĠGoogle Ġdatasets Ġ( long Ġtext ). </s> We Ġnext Ġvisualize Ġthe Ġdetected Ġevents Ġbased Ġon Ġthe Ġdiscrim in ative Ġfeatures Ġlearned Ġby Ġthe Ġtrained ĠIN LINE FORM 0 Ġnetwork Ġin ĠA EM . ĠThe Ġt - S NE ĠB IB REF 16 Ġvisualization Ġresults Ġon Ġthe Ġdatasets Ġare Ġshown Ġin ĠFigure ĠFIG REF 19 Ġ. ĠFor Ġclarity , Ġeach Ġsub plot Ġis Ġplotted Ġon Ġa Ġsubset Ġof Ġthe Ġdataset Ġcontaining Ġten Ġrandomly Ġselected Ġevents . ĠIt Ġcan Ġbe Ġobserved Ġthat Ġdocuments Ġdescribing Ġthe Ġsame Ġevent Ġhave Ġbeen Ġgrouped Ġinto Ġthe Ġsame Ġcluster . </s> To Ġfurther Ġevaluate Ġif Ġa Ġvariation Ġof Ġthe Ġparameters ĠIN LINE FORM 0 Ġ( the Ġnumber Ġof Ġdiscrim inator Ġiterations Ġper Ġgenerator Ġiteration ), ĠIN LINE FORM 1 Ġ( the Ġnumber Ġof Ġunits Ġin Ġhidden Ġlayer ) Ġand Ġthe Ġstructure Ġof Ġgenerator ĠIN LINE FORM 2 Ġwill Ġimpact Ġthe Ġextraction Ġperformance , Ġadditional Ġexperiments Ġhave Ġbeen Ġconducted Ġon Ġthe ĠGoogle Ġdataset , Ġwith ĠIN LINE FORM 3 Ġset Ġto Ġ5 , Ġ7 Ġand Ġ10 , ĠIN LINE FORM 4 Ġset Ġto Ġ100 , Ġ150 Ġand Ġ200 , Ġand Ġthree ĠIN LINE FORM 5 Ġstructures Ġ( 3 , Ġ4 Ġand Ġ5 Ġlayers ). ĠThe Ġcomparison Ġresults Ġon Ġprecision , Ġrecall Ġand ĠF - me asure Ġare Ġshown Ġin ĠFigure ĠFIG REF 20 Ġ. ĠFrom Ġthe Ġresults , Ġit Ġcould Ġbe Ġobserved Ġthat ĠA EM Ġwith Ġthe Ġ5 - layer Ġgenerator Ġperforms Ġthe Ġbest Ġand Ġachieves Ġ96 . 7 % Ġin ĠF - me asure , Ġand Ġthe Ġworst ĠF - me asure Ġobtained Ġby ĠA EM Ġis Ġ85 . 7 %. ĠOverall , Ġthe ĠA EM Ġoutper forms Ġall Ġcompared Ġapproaches Ġac ors s Ġvarious Ġparameter Ġsettings , Ġshowing Ġrelatively Ġstable Ġperformance . </s> Finally , Ġwe Ġcompare Ġin ĠFigure ĠFIG REF 37 Ġthe Ġtraining Ġtime Ġrequired Ġfor Ġeach Ġmodel , Ġexcluding Ġthe Ġconstant Ġtime Ġrequired Ġby Ġeach Ġmodel Ġto Ġload Ġthe Ġdata . ĠWe Ġcould Ġobserve Ġthat ĠK - me ans Ġruns Ġfastest Ġamong Ġall Ġfour Ġapproaches . ĠBoth ĠL EM Ġand ĠDP EM M Ġneed Ġto Ġsample Ġthe Ġevent Ġallocation Ġfor Ġeach Ġdocument Ġand Ġupdate Ġthe Ġrelevant Ġcounts Ġduring ĠGibbs Ġsampling Ġwhich Ġare Ġtime Ġconsuming . ĠA EM Ġonly Ġrequires Ġa Ġfraction Ġof Ġthe Ġtraining Ġtime Ġcompared Ġto ĠL EM Ġand ĠDP EM M . ĠMoreover , Ġon Ġa Ġlarger Ġdataset Ġsuch Ġas Ġthe ĠGoogle Ġdataset , ĠA EM Ġappears Ġto Ġbe Ġfar Ġmore Ġefficient Ġcompared Ġto ĠL EM Ġand ĠDP EM M . </s> Con clusions Ġand ĠFuture ĠWork </s> In Ġthis Ġpaper , Ġwe Ġhave Ġproposed Ġa Ġnovel Ġapproach Ġbased Ġon Ġadvers arial Ġtraining Ġto Ġextract Ġthe Ġstructured Ġrepresentation Ġof Ġevents Ġfrom Ġonline Ġtext . ĠThe Ġexperimental Ġcomparison Ġwith Ġthe Ġstate - of - the - art Ġmethods Ġshows Ġthat ĠA EM Ġachieves Ġimproved Ġextraction Ġperformance , Ġespecially Ġon Ġlong Ġtext Ġcorpor a Ġwith Ġan Ġimprovement Ġof Ġ15 % Ġobserved Ġin ĠF - me asure . ĠA EM Ġonly Ġrequires Ġa Ġfraction Ġof Ġtraining Ġtime Ġcompared Ġto Ġexisting ĠBay esian Ġgraphical Ġmodeling Ġapproaches . ĠIn Ġfuture Ġwork , Ġwe Ġwill Ġexplore Ġincorporating Ġexternal Ġknowledge Ġ( e . g . Ġword Ġrelated ness Ġcontained Ġin Ġword Ġembed d ings ) Ġinto Ġthe Ġlearning Ġframework Ġfor Ġevent Ġextraction . ĠBesides , Ġexploring Ġnon param etric Ġneural Ġevent Ġextraction Ġapproaches Ġand Ġdetecting Ġthe Ġevolution Ġof Ġevents Ġover Ġtime Ġfrom Ġnews Ġarticles Ġare Ġother Ġpromising Ġfuture Ġdirections . </s> Acknowled gments </s> We Ġwould Ġlike Ġto Ġthank Ġanonymous Ġreviewers Ġfor Ġtheir Ġvaluable Ġcomments Ġand Ġhelpful Ġsuggestions . ĠThis Ġwork Ġwas Ġfunded Ġby Ġthe ĠNational ĠKey ĠResearch Ġand ĠDevelopment ĠProgram Ġof ĠChina Ġ( 2016 Y FC 130 6 704 ), Ġthe ĠNational ĠNatural ĠScience ĠFoundation Ġof ĠChina Ġ( 617 72 132 ), Ġthe ĠNatural ĠScience ĠFoundation Ġof ĠJiang su ĠProvince Ġof ĠChina Ġ( B K 2016 14 30 ).