selection of word vectors
EXTRACTIVE
In this paper, we explore a hybrid approach, that uses text embeddings as a proxy to create features. Motivated by the argument that text embeddings manage to encode the semantics of text, we explore how clustering text embeddings can impact the performance of different NLP tasks. Although such an approach has been used in different studies during feature engineering, the selection of word vectors and the number of clusters remain a trial-end-error procedure. In this work we present an empirical evaluation across diverse tasks to verify whether and when such features are useful.
1705.01265
Introduction </s> Many Ġresearch Ġattempts Ġhave Ġproposed Ġnovel Ġfeatures Ġthat Ġimprove Ġthe Ġperformance Ġof Ġlearning Ġalgorithms Ġin Ġparticular Ġtasks . ĠSuch Ġfeatures Ġare Ġoften Ġmotivated Ġby Ġdomain Ġknowledge Ġor Ġmanual Ġlabor . ĠAlthough Ġuseful Ġand Ġoften Ġstate - of - the - art , Ġadapting Ġsuch Ġsolutions Ġon ĠN LP Ġsystems Ġacross Ġtasks Ġcan Ġbe Ġtricky Ġand Ġtime - consuming ĠB IB REF 0 Ġ. ĠTherefore , Ġsimple Ġyet Ġgeneral Ġand Ġpowerful Ġmethods Ġthat Ġperform Ġwell Ġacross Ġseveral Ġdatasets Ġare Ġvaluable ĠB IB REF 1 Ġ. </s> An Ġapproach Ġthat Ġhas Ġbecome Ġextremely Ġpopular Ġlately Ġin ĠN LP Ġtasks , Ġis Ġto Ġtrain Ġword Ġembed d ings Ġin Ġan Ġun super vised Ġway . ĠThese Ġembed d ings Ġare Ġdense Ġvectors Ġthat Ġproject Ġwords Ġor Ġshort Ġtext Ġspans Ġlike Ġphrases Ġin Ġa Ġvector Ġspace Ġwhere Ġdimensions Ġare Ġsupposed Ġto Ġcapture Ġtext Ġproperties . ĠSuch Ġembed d ings Ġcan Ġthen Ġbe Ġused Ġeither Ġas Ġfeatures Ġwith Ġoff - the - she lf Ġalgorithms Ġlike ĠSupport ĠVector ĠMachines , Ġor Ġto Ġinitialize Ġdeep Ġlearning Ġsystems ĠB IB REF 2 Ġ. ĠHowever , Ġas Ġshown Ġin ĠB IB REF 3 Ġlinear Ġarchitectures Ġperform Ġbetter Ġin Ġhigh - dimensional Ġdiscrete Ġspaces Ġcompared Ġto Ġcontinuous Ġones . ĠThe Ġlatter Ġis Ġprobably Ġthe Ġmain Ġreason Ġof Ġthe Ġhigh Ġperformance Ġof Ġthe Ġvector Ġspace Ġmodel ĠB IB REF 4 Ġin Ġtasks Ġlike Ġtext Ġclassification Ġwith Ġlinear Ġmodels Ġlike ĠSV Ms . ĠUsing Ġlinear Ġalgorithms , Ġwhile Ġtaking Ġadvantage Ġof Ġthe Ġexpress iveness Ġof Ġtext Ġembed d ings Ġis Ġthe Ġfocus Ġof Ġthis Ġwork . </s> In Ġthis Ġpaper , Ġwe Ġexplore Ġa Ġhybrid Ġapproach , Ġthat Ġuses Ġtext Ġembed d ings Ġas Ġa Ġproxy Ġto Ġcreate Ġfeatures . ĠMot ivated Ġby Ġthe Ġargument Ġthat Ġtext Ġembed d ings Ġmanage Ġto Ġencode Ġthe Ġsemantics Ġof Ġtext , Ġwe Ġexplore Ġhow Ġclust ering Ġtext Ġembed d ings Ġcan Ġimpact Ġthe Ġperformance Ġof Ġdifferent ĠN LP Ġtasks . ĠAlthough Ġsuch Ġan Ġapproach Ġhas Ġbeen Ġused Ġin Ġdifferent Ġstudies Ġduring Ġfeature Ġengineering , Ġthe Ġselection Ġof Ġword Ġvectors Ġand Ġthe Ġnumber Ġof Ġclusters Ġremain Ġa Ġtrial - end - error Ġprocedure . ĠIn Ġthis Ġwork Ġwe Ġpresent Ġan Ġempirical Ġevaluation Ġacross Ġdiverse Ġtasks Ġto Ġverify Ġwhether Ġand Ġwhen Ġsuch Ġfeatures Ġare Ġuseful . </s> Word Ġclusters Ġhave Ġbeen Ġused Ġas Ġfeatures Ġin Ġvarious Ġtasks Ġlike ĠPart - of - Spe ech Ġtagging Ġand ĠN ER . ĠOw op uti Ġet Ġal . ĠOw op uti 13 Ġuse ĠBrown Ġclusters ĠB IB REF 5 Ġin Ġa ĠPOS Ġtag ger Ġshowing Ġthat Ġthis Ġtype Ġof Ġfeatures Ġcarry Ġrich Ġlex ical Ġknowledge Ġas Ġthey Ġcan Ġsubstitute Ġlex ical Ġresources Ġlike Ġg az ette ers . ĠK irit chenko Ġet Ġal . ĠK irit chenko Z M 14 Ġdiscusses Ġtheir Ġuse Ġon Ġsentiment Ġclassification Ġwhile ĠHe e Ġet Ġal . ĠHe e L H 16 Ġincorporate Ġthem Ġin Ġthe Ġtask Ġof Ġirony Ġdetection Ġin ĠTwitter . ĠR itter Ġet Ġal . ĠR itter 2011 Ġinject Ġalso Ġword Ġclusters Ġin Ġa ĠN ER Ġtag ger . ĠWhile Ġthese Ġworks Ġshow Ġthat Ġword Ġclusters Ġare Ġbeneficial Ġno Ġclear Ġguidelines Ġcan Ġbe Ġconcluded Ġof Ġhow Ġand Ġwhen Ġto Ġuse Ġthem . </s> In Ġthis Ġwork , Ġwe Ġempir ically Ġdemonstrate Ġthat Ġusing Ġdifferent Ġtypes Ġof Ġembed d ings Ġon Ġthree ĠN LP Ġtasks Ġwith Ġtwitter Ġdata Ġwe Ġmanage Ġto Ġachieve Ġbetter Ġor Ġnear Ġto Ġthe Ġstate - of - the Ġart Ġperformance Ġon Ġthree ĠN LP Ġtasks : Ġ( i ) ĠNamed ĠEntity ĠRecogn ition Ġ( NER ) Ġsegment ation , Ġ( ii ) ĠN ER Ġclassification , Ġ( iii ) Ġfine - gr ained Ġsentiment Ġanalysis Ġand Ġ( iv ) Ġfine - gr ained Ġsentiment Ġquant ification . ĠFor Ġeach Ġof Ġthe Ġthree Ġtasks , Ġwe Ġachieve Ġhigher Ġperformance Ġthan Ġwithout Ġusing Ġfeatures Ġwhich Ġindicates Ġthe Ġeffectiveness Ġof Ġthe Ġcluster Ġmembership Ġfeatures . ĠImport antly , Ġour Ġevaluation Ġcompared Ġto Ġprevious Ġwork ĠB IB REF 6 Ġwho Ġfocus Ġon Ġold Ġand Ġwell Ġstudied Ġdatasets Ġuses Ġrecent Ġand Ġchallenging Ġdatasets Ġcomposed Ġby Ġtweets . ĠThe Ġobtained Ġresults Ġacross Ġall Ġthe Ġtasks Ġpermits Ġus Ġto Ġreveal Ġimportant Ġaspects Ġof Ġthe Ġuse Ġof Ġword Ġclusters Ġand Ġtherefore Ġprovide Ġguidelines . ĠAlthough Ġour Ġobtained Ġscores Ġare Ġstate - of - the - art , Ġour Ġanalysis Ġreveals Ġthat Ġthe Ġperformance Ġin Ġsuch Ġtasks Ġis Ġfar Ġfrom Ġperfect Ġand , Ġhence , Ġidentifies Ġthat Ġthere Ġis Ġstill Ġmuch Ġspace Ġfor Ġimprovement Ġand Ġfuture Ġwork . </s> Word ĠCl usters </s> Word Ġembed d ings Ġassociate Ġwords Ġwith Ġdense , Ġlow - dimensional Ġvectors . ĠRecently , Ġseveral Ġmodels Ġhave Ġbeen Ġproposed Ġin Ġorder Ġto Ġobtain Ġthese Ġembed d ings . ĠAmong Ġothers , Ġthe Ġskip gram Ġ( skip gram ) Ġmodel Ġwith Ġnegative Ġsampling ĠB IB REF 7 Ġ, Ġthe Ġcontinuous Ġbag - of - words Ġ( c bow ) Ġmodel ĠB IB REF 7 Ġand ĠGl ove Ġ( gl ove ) ĠB IB REF 8 Ġhave Ġbeen Ġshown Ġto Ġbe Ġeffective . ĠTraining Ġthose Ġmodels Ġrequires Ġno Ġannot ated Ġdata Ġand Ġcan Ġbe Ġdone Ġusing Ġbig Ġamounts Ġof Ġtext . ĠSuch Ġa Ġmodel Ġcan Ġbe Ġseen Ġas Ġa Ġfunction ĠIN LINE FORM 0 Ġthat Ġprojects Ġa Ġword ĠIN LINE FORM 1 Ġin Ġa ĠIN LINE FORM 2 Ġ- dimensional Ġspace : ĠIN LINE FORM 3 Ġ, Ġwhere ĠIN LINE FORM 4 Ġis Ġpred efined . ĠHere , Ġwe Ġfocus Ġon Ġapplications Ġusing Ġdata Ġfrom ĠTwitter , Ġwhich Ġpose Ġseveral Ġdifficulties Ġdue Ġto Ġbeing Ġparticularly Ġshort , Ġusing Ġcreative Ġvocabulary , Ġabbrevi ations Ġand Ġslang . </s> For Ġall Ġthe Ġtasks Ġin Ġour Ġexperimental Ġstudy , Ġwe Ġuse Ġ36 Ġmillions ĠEnglish Ġtweets Ġcollected Ġbetween ĠAugust Ġand ĠSeptember Ġ2017 . ĠA Ġpre - processing Ġstep Ġhas Ġbeen Ġapplied Ġto Ġreplace ĠURLs Ġwith Ġa Ġplaceholder Ġand Ġto Ġpad Ġpunct uation . ĠThe Ġfinal Ġvocabulary Ġsize Ġwas Ġaround Ġ1 . 6 Ġmillions Ġwords . ĠAdditionally Ġto Ġthe Ġin - domain Ġcorpus Ġwe Ġcollected , Ġwe Ġuse ĠGl o Ve Ġvectors Ġtrained Ġon ĠWikipedia Ġarticles Ġin Ġorder Ġto Ġinvestigate Ġthe Ġimpact Ġof Ġout - of - domain Ġword - ve ctors . </s> We Ġcluster Ġthe Ġembed d ings Ġwith ĠIN LINE FORM 0 Ġ- Me ans . ĠThe Ġk - me ans Ġclusters Ġare Ġinitialized Ġusing ĠâĢ ľ k - me ans ++ âĢ Ŀ Ġas Ġproposed Ġin ĠB IB REF 9 Ġ, Ġwhile Ġthe Ġalgorithm Ġis Ġrun Ġfor Ġ300 Ġiterations . ĠWe Ġtry Ġdifferent Ġvalues Ġfor ĠIN LINE FORM 1 Ġ. ĠFor Ġeach ĠIN LINE FORM 2 Ġ, Ġwe Ġrepeat Ġthe Ġclust ering Ġexperiment Ġwith Ġdifferent Ġseed Ġinitialization Ġfor Ġ10 Ġtimes Ġand Ġwe Ġselect Ġthe Ġclust ering Ġresult Ġthat Ġminim izes Ġthe Ġcluster Ġinertia . </s> Exper imental ĠEvaluation </s> We Ġevaluate Ġthe Ġproposed Ġapproach Ġfor Ġaugment ing Ġthe Ġfeature Ġspace Ġin Ġfour Ġtasks : Ġ( i ) ĠN ER Ġsegment ation , Ġ( ii ) ĠN ER Ġclassification , Ġ( iii ) Ġfine - gr ained Ġsentiment Ġclassification Ġand Ġ( iv ) Ġfine - gr ained Ġsentiment Ġquant ification . ĠThe Ġnext Ġsections Ġpresent Ġthe Ġevaluation Ġsettings Ġwe Ġused . ĠFor Ġeach Ġof Ġthe Ġtasks , Ġwe Ġuse Ġthe Ġdesignated Ġtraining Ġsets Ġto Ġtrain Ġthe Ġlearning Ġalgorithms , Ġand Ġwe Ġreport Ġthe Ġscores Ġof Ġthe Ġevaluation Ġmeasures Ġused Ġin Ġthe Ġrespective Ġtest Ġparts . </s> N amed - Entity ĠRecogn ition Ġin ĠTwitter </s> NER Ġconcerns Ġthe Ġclassification Ġof Ġtextual Ġsegments Ġin Ġa Ġpred efined Ġset Ġof Ġcategories , Ġlike Ġpersons , Ġorganization Ġand Ġlocations . ĠWe Ġuse Ġthe Ġdata Ġof Ġthe Ġlast Ġcompetition Ġin ĠN ER Ġfor ĠTwitter Ġwhich Ġreleased Ġas Ġa Ġpart Ġof Ġthe Ġ2 nd ĠWorkshop Ġon ĠNo isy ĠUser - generated ĠText ĠB IB REF 10 Ġ. ĠMore Ġspecifically , Ġthe Ġorganizers Ġprovided Ġannot ated Ġtweets Ġwith Ġ10 Ġnamed - entity Ġtypes Ġ( person , Ġmovie , Ġsport steam , Ġproduct Ġetc .) Ġand Ġthe Ġtask Ġcomprised Ġtwo Ġsub - t asks : Ġ1 ) Ġthe Ġdetection Ġof Ġentity Ġbounds Ġand Ġ2 ) Ġthe Ġclassification Ġof Ġan Ġentity Ġinto Ġone Ġof Ġthe Ġ10 Ġtypes . ĠThe Ġevaluation Ġmeasure Ġfor Ġboth Ġsub - t asks Ġis Ġthe ĠF ĠIN LINE FORM 0 Ġmeasure . </s> The Ġfollowing Ġis Ġan Ġexample Ġof Ġa Ġtweet Ġwhich Ġcontains Ġtwo Ġnamed Ġentities . ĠNote Ġthat Ġnamed Ġentities Ġmay Ġspan Ġseveral Ġwords Ġin Ġthe Ġtext : </s> IN LINE FORM 0 Ġton ite Ġ... Ġ90 Ġ' s Ġmusic Ġ.. Ġold sk ool Ġnight Ġwi ith ĠIN LINE FORM 1 </s> Our Ġmodel Ġfor Ġsolving Ġthe Ġtask Ġis Ġa Ġlearning Ġto Ġsearch Ġapproach . ĠMore Ġspecifically Ġwe Ġfollow ĠB IB REF 11 Ġwhich Ġhas Ġbeen Ġranked Ġ2 nd Ġamong Ġ10 Ġparticipants Ġin Ġthe Ġaforementioned Ġcompetition ĠB IB REF 10 Ġ. ĠThe Ġmodel Ġuses Ġhand crafted Ġfeatures Ġlike Ġn - gram s , Ġpart - of - speech Ġtags , Ġcapital ization Ġand Ġmembership Ġin Ġg az ette ers . ĠThe Ġalgorithm Ġused Ġbelongs Ġto Ġthe Ġfamily Ġof Ġlearning Ġto Ġsearch Ġfor Ġstructured Ġprediction Ġtasks ĠB IB REF 12 Ġ. ĠThese Ġmethods Ġdecom pose Ġthe Ġproblem Ġin Ġa Ġsearch Ġspace Ġwith Ġstates , Ġactions Ġand Ġpolicies Ġand Ġthen Ġlearn Ġa Ġhypothesis Ġcontrolling Ġa Ġpolicy Ġover Ġthe Ġstate - action Ġspace . ĠThe ĠB IO Ġencoding Ġis Ġused Ġfor Ġattribut ing Ġthe Ġcorresponding Ġlabels Ġto Ġthe Ġtokens Ġwhere ĠB - type Ġis Ġused Ġfor Ġthe Ġfirst Ġtoken Ġof Ġthe Ġentity , ĠI - type Ġfor Ġinside Ġtokens Ġin Ġcase Ġof Ġmulti - term Ġentities Ġand ĠO Ġfor Ġnon Ġentity Ġtokens . </s> T ables ĠT AB REF 6 Ġand ĠT AB REF 7 Ġpresent Ġthe Ġresults Ġfor Ġthe Ġdifferent Ġnumber Ġof Ġclusters Ġacross Ġthe Ġthree Ġvector Ġmodels Ġused Ġto Ġinduce Ġthe Ġclusters . ĠFor Ġall Ġthe Ġexperiments Ġwe Ġkeep Ġthe Ġsame Ġparam et riz ation Ġfor Ġthe Ġlearning Ġalgorithm Ġand Ġwe Ġpresent Ġthe Ġperformance Ġof Ġeach Ġrun Ġon Ġthe Ġofficial Ġtest Ġset . </s> Regarding Ġthe Ġsegment ation Ġtask Ġwe Ġnotice Ġthat Ġadding Ġword Ġclusters Ġas Ġfeatures Ġimprove Ġthe Ġperformance Ġof Ġthe Ġbest Ġmodel Ġup Ġto Ġ1 . 1 ĠF - score Ġpoints Ġwhile Ġit Ġboosts Ġperformance Ġin Ġthe Ġmajority Ġof Ġcases . ĠIn Ġonly Ġone Ġcase , Ġfor Ġglove ĠIN LINE FORM 0 Ġvectors , Ġthere Ġis Ġa Ġdrop Ġacross Ġall Ġnumber Ġof Ġclusters Ġused . </s> As Ġfor Ġthe Ġnumber Ġof Ġclusters , Ġthe Ġbest Ġresults Ġare Ġgenerally Ġobtained Ġbetween Ġ250 Ġand Ġ1000 Ġclasses Ġfor Ġall Ġword Ġvector Ġmodels . ĠThese Ġdimensions Ġseem Ġto Ġbe Ġsufficient Ġfor Ġthe Ġthree - class Ġsub - task Ġthat Ġwe Ġdeal Ġwith . ĠThe Ġdifferent Ġmodels Ġof Ġword Ġvectors Ġperform Ġsimilarly Ġand Ġthus Ġone Ġcannot Ġprivilege Ġa Ġcertain Ġtype Ġof Ġword Ġvectors . ĠInterestingly , Ġthe Ġclusters Ġlearned Ġon Ġthe ĠWikipedia ĠGl o Ve Ġvectors Ġoffer Ġcompetitive Ġperformance Ġwith Ġrespect Ġto Ġthe Ġin - domain Ġword Ġvectors Ġused Ġfor Ġthe Ġother Ġcases Ġshowing Ġthat Ġone Ġcan Ġrely Ġto Ġout - of - domain Ġdata Ġfor Ġconstructing Ġsuch Ġrepresentations . </s> Con cerning Ġthe Ġclassification Ġtask Ġ( Table ĠT AB REF 7 Ġ) Ġwe Ġgenerally Ġobserve Ġa Ġdrop Ġin Ġthe Ġperformance Ġof Ġthe Ġtag ger Ġas Ġwe Ġdeal Ġwith Ġ10 Ġclasses . ĠThis Ġessentially Ġcorresponds Ġto Ġa Ġmulti - class Ġproblem Ġwith Ġ21 Ġclasses : Ġone Ġfor Ġthe Ġnon - entity Ġtype Ġand Ġtwo Ġclasses Ġfor Ġeach Ġentity Ġtype . ĠIn Ġthis Ġsetting Ġwe Ġnotice Ġthat Ġthe Ġbest Ġresults Ġare Ġobtained Ġin Ġmost Ġcases Ġfor Ġhigher Ġnumber Ġof Ġclasses Ġ( 1000 Ġor Ġ2000 ) Ġpossibly Ġdue Ġto Ġa Ġbetter Ġdiscriminatory Ġpower Ġin Ġhigher Ġdimensions . ĠNote Ġalso , Ġthat Ġin Ġsome Ġcases Ġthe Ġaddition Ġof Ġword Ġcluster Ġfeatures Ġdoes Ġnot Ġnecessarily Ġimprove Ġthe Ġperformance . ĠContrary , Ġit Ġmay Ġdegrade Ġit Ġas Ġit Ġis Ġevident Ġin Ġthe Ġcase Ġof Ġglove ĠIN LINE FORM 0 Ġword Ġclusters . ĠLike Ġin Ġthe Ġcase Ġof Ġsegment ation Ġwe Ġdo Ġnot Ġobserve Ġa Ġword Ġvector Ġmodel Ġthat Ġclearly Ġoutper forms Ġthe Ġrest . ĠFinally , Ġwe Ġnote Ġthe Ġsame Ġcompetitive Ġperformance Ġof Ġthe ĠWikipedia Ġword Ġclusters Ġand Ġnotably Ġfor Ġthe Ġglove ĠIN LINE FORM 1 Ġclusters Ġwhich Ġobtain Ġthe Ġbest ĠF 1 - score . </s> Fine - gr ained ĠSent iment ĠAnalysis </s> The Ġtask Ġof Ġfine Ġgr ained Ġsentiment Ġclassification Ġconsists Ġin Ġpredicting Ġthe Ġsentiment Ġof Ġan Ġinput Ġtext Ġaccording Ġto Ġa Ġfive Ġpoint Ġscale Ġ( sent iment ĠIN LINE FORM 0 Ġ{ Very Neg ative , ĠNegative , ĠNeutral , ĠPositive , ĠVery Pos itive } ). ĠWe Ġuse Ġthe Ġsetting Ġof Ġtask Ġ4 Ġof ĠSem E val 2016 ĠâĢ ľ Sent iment ĠAnalysis Ġin ĠTwitter âĢ Ŀ Ġand Ġthe Ġdataset Ġreleased Ġby Ġthe Ġorganizers Ġfor Ġsubt ask Ġ4 ĠB IB REF 13 Ġ. </s> In Ġtotal , Ġthe Ġtraining Ġ( resp . Ġtest ) Ġdata Ġconsist Ġof Ġ9 , 070 Ġ( resp . Ġ20 , 6 32 ) Ġtweets . </s> The Ġevaluation Ġmeasure Ġselected Ġin ĠB IB REF 13 Ġfor Ġthe Ġtask Ġin Ġthe Ġmacro - aver aged ĠMean ĠAbsolute ĠError Ġ( MA E ĠIN LINE FORM 0 Ġ). ĠIt Ġis Ġa Ġmeasure Ġof Ġerror , Ġhence Ġlower Ġvalues Ġare Ġbetter . ĠThe Ġmeasure 's Ġgoal Ġis Ġto Ġtake Ġinto Ġaccount Ġthe Ġorder Ġof Ġthe Ġclasses Ġwhen Ġpenal izing Ġthe Ġdecision Ġof Ġa Ġclass ifier . ĠFor Ġinstance , Ġmis class ifying Ġa Ġvery Ġnegative Ġexample Ġas Ġvery Ġpositive Ġis Ġa Ġbigger Ġmistake Ġthan Ġclass ifying Ġit Ġas Ġnegative Ġor Ġneutral . ĠPenal izing Ġa Ġclass ifier Ġaccording Ġto Ġhow Ġfar Ġthe Ġpredictions Ġare Ġfrom Ġthe Ġtrue Ġclass Ġis Ġcaptured Ġby ĠMA E ĠIN LINE FORM 1 ĠB IB REF 14 Ġ. ĠAlso , Ġthe Ġadvantage Ġof Ġusing Ġthe Ġmacro - Ġversion Ġinstead Ġof Ġthe Ġstandard Ġversion Ġof Ġthe Ġmeasure Ġis Ġthe Ġrobust ness Ġagainst Ġthe Ġclass Ġimbalance Ġin Ġthe Ġdata . </s> Learning Ġalgorithm ĠTo Ġdemonstrate Ġthe Ġefficiency Ġof Ġcluster Ġmembership Ġfeatures Ġwe Ġrely Ġon Ġthe Ġsystem Ġof ĠB IB REF 15 Ġwhich Ġwas Ġranked Ġ1 st Ġamong Ġ11 Ġparticipants Ġand Ġuses Ġa ĠLog istic ĠReg ression Ġas Ġa Ġlearning Ġalgorithm . ĠWe Ġfollow Ġthe Ġsame Ġfeature Ġextraction Ġsteps Ġwhich Ġconsist Ġof Ġextracting Ġn - gram Ġand Ġcharacter Ġn - gram Ġfeatures , Ġpart - of - speech Ġcounts Ġas Ġwell Ġas Ġsentiment Ġscores Ġusing Ġstandard Ġsentiment Ġlex icons Ġsuch Ġas Ġthe ĠBing ĠLiu 's ĠB IB REF 16 Ġand Ġthe ĠMP Q A Ġlex icons ĠB IB REF 17 Ġ. ĠFor Ġthe Ġfull Ġdescription , Ġwe Ġrefer Ġthe Ġinterested Ġreader Ġto ĠB IB REF 15 Ġ. </s> To Ġevaluate Ġthe Ġperformance Ġof Ġthe Ġproposed Ġfeature Ġaug mentation Ġtechnique , Ġwe Ġpresent Ġin ĠTable ĠT AB REF 10 Ġthe Ġmacro - aver aged ĠMean ĠAbsolute ĠError Ġscores Ġfor Ġdifferent Ġsettings Ġon Ġthe Ġofficial Ġtest Ġset Ġof ĠB IB REF 13 Ġ. ĠFirst , Ġnotice Ġthat Ġthe Ġbest Ġscore Ġin Ġthe Ġtest Ġdata Ġis Ġachieved Ġusing Ġcluster Ġmembership Ġfeatures , Ġwhere Ġthe Ġword Ġembed d ings Ġare Ġtrained Ġusing Ġthe Ġskip gram Ġmodel . ĠThe Ġachieved Ġscore Ġimproves Ġthe Ġstate - of - the Ġart Ġon Ġthe Ġdataset , Ġwhich Ġto Ġthe Ġbest Ġof Ġour Ġknowledge Ġwas Ġby ĠB IB REF 15 Ġ. ĠAlso , Ġnote Ġthat Ġthe Ġscore Ġon Ġthe Ġtest Ġdata Ġimproves Ġfor Ġeach Ġtype Ġof Ġembed d ings Ġused , Ġwhich Ġmeans Ġthat Ġaugment ing Ġthe Ġfeature Ġspace Ġusing Ġcluster Ġmembership Ġfeatures Ġhelps Ġthe Ġsentiment Ġclassification Ġtask . </s> Note , Ġalso , Ġthat Ġusing Ġthe Ġclusters Ġproduced Ġby Ġthe Ġout - of - domain Ġembed d ings Ġtrained Ġon Ġwik ipedia Ġthat Ġwere Ġreleased Ġas Ġpart Ġof ĠB IB REF 8 Ġperforms Ġsurprisingly Ġwell . ĠOne Ġmight Ġhave Ġexpected Ġtheir Ġaddition Ġto Ġhurt Ġthe Ġperformance . ĠHowever , Ġtheir Ġvalue Ġprobably Ġstems Ġfrom Ġthe Ġsheer Ġamount Ġof Ġdata Ġused Ġfor Ġtheir Ġtraining Ġas Ġwell Ġas Ġthe Ġrelatively Ġsimple Ġtype Ġof Ġwords Ġ( like Ġawesome , Ġterrible ) Ġwhich Ġare Ġdiscrim in ative Ġfor Ġthis Ġtask . ĠLastly , Ġnote Ġthat Ġin Ġeach Ġof Ġthe Ġsettings , Ġthe Ġbest Ġresults Ġare Ġachieved Ġwhen Ġthe Ġnumber Ġof Ġclusters Ġis Ġwithin ĠIN LINE FORM 0 Ġas Ġin Ġthe ĠN ER Ġtasks . ĠComp aring Ġthe Ġperformance Ġacross Ġthe Ġdifferent Ġembed d ings , Ġone Ġcannot Ġclaim Ġthat Ġa Ġparticular Ġembed ding Ġperforms Ġbetter . ĠIt Ġis Ġevident Ġthough Ġthat Ġaugment ing Ġthe Ġfeature Ġspace Ġwith Ġfeature Ġderived Ġusing Ġthe Ġproposed Ġmethod , Ġpreferably Ġwith Ġin - domain Ġdata , Ġhelps Ġthe Ġclassification Ġperformance Ġand Ġreduces ĠMA E ĠIN LINE FORM 1 Ġ. </s> From Ġthe Ġresults Ġof ĠTable ĠT AB REF 10 Ġit Ġis Ġclear Ġthat Ġthe Ġaddition Ġof Ġthe Ġcluster Ġmembership Ġfeatures Ġimproves Ġthe Ġsentiment Ġclassification Ġperformance . ĠTo Ġbetter Ġunderstand Ġthough Ġwhy Ġthese Ġclusters Ġhelp , Ġwe Ġmanually Ġexamined Ġa Ġsample Ġof Ġthe Ġwords Ġassociated Ġwith Ġthe Ġclusters . ĠTo Ġimprove Ġthe Ġeligibility Ġof Ġthose Ġresults Ġwe Ġfirst Ġremoved Ġthe Ġhasht ags Ġand Ġwe Ġfilter Ġthe Ġresults Ġusing Ġan ĠEnglish Ġvocabulary . ĠIn ĠTable ĠT AB REF 11 Ġwe Ġpresent Ġsample Ġwords Ġfrom Ġtwo Ġof Ġthe Ġmost Ġcharacteristic Ġclusters Ġwith Ġrespect Ġto Ġthe Ġtask Ġof Ġsentiment Ġclassification . ĠNotice Ġhow Ġwords Ġwith Ġpositive Ġand Ġnegative Ġmeanings Ġare Ġput Ġin Ġthe Ġrespective Ġclusters . </s> Fine - Gr ained ĠSent iment ĠQuant ification </s> Quant ification Ġis Ġthe Ġproblem Ġof Ġestimating Ġthe Ġprevalence Ġof Ġa Ġclass Ġin Ġa Ġdataset . ĠWhile Ġclassification Ġconcerns Ġassigning Ġa Ġcategory Ġto Ġa Ġsingle Ġinstance , Ġlike Ġlabeling Ġa Ġtweet Ġwith Ġthe Ġsentiment Ġit Ġconve ys , Ġthe Ġgoal Ġof Ġquant ification Ġis , Ġgiven Ġa Ġset Ġof Ġinstances , Ġto Ġestimate Ġthe Ġrelative Ġfrequency Ġof Ġsingle Ġclass . ĠTherefore , Ġsentiment Ġquant ification Ġtries Ġto Ġanswer Ġquestions Ġlike ĠâĢ ľ Given Ġa Ġset Ġof Ġtweets Ġabout Ġthe Ġnew ĠiPhone , Ġwhat Ġis Ġthe Ġfraction Ġof ĠVery Pos itive Ġones ? âĢ Ŀ . ĠIn Ġthe Ġrest , Ġwe Ġshow Ġthe Ġeffect Ġof Ġthe Ġfeatures Ġderived Ġfrom Ġthe Ġword Ġembed d ings Ġclusters Ġin Ġthe Ġfine - gr ained Ġclassification Ġproblem , Ġwhich Ġwas Ġalso Ġpart Ġof Ġthe ĠSem E val - 2016 ĠâĢ ľ Sent iment ĠAnalysis Ġin ĠTwitter âĢ Ŀ Ġtask ĠB IB REF 13 Ġ. </s> Learning ĠAl gorithm ĠTo Ġperform Ġthe Ġquant ification Ġtask , Ġwe Ġrely Ġon Ġa Ġclassify Ġand Ġcount Ġapproach , Ġwhich Ġwas Ġshown Ġeffective Ġin Ġa Ġrelated Ġbinary Ġquant ification Ġproblem ĠB IB REF 15 Ġ. ĠThe Ġidea Ġis Ġthat Ġgiven Ġa Ġset Ġof Ġinstances Ġon Ġa Ġparticular Ġsubject , Ġone Ġfirst Ġclass ifies Ġthe Ġinstances Ġand Ġthen Ġaggreg ates Ġthe Ġcounts . ĠTo Ġthis Ġend , Ġwe Ġuse Ġthe Ġsame Ġfeature Ġrepresentation Ġsteps Ġand Ġdata Ġwith Ġthe Ġones Ġused Ġfor Ġfine Ġgr ained Ġclassification Ġ( Section Ġ3 . 2 ). ĠNote Ġthat Ġthe Ġdata Ġof Ġthe Ġtask Ġare Ġassociated Ġwith Ġsubjects Ġ( described Ġin Ġfull Ġdetail Ġat ĠB IB REF 13 Ġ), Ġand , Ġhence , Ġquant ification Ġis Ġperformed Ġfor Ġthe Ġtweets Ġof Ġa Ġsubject . ĠFor Ġeach Ġof Ġthe Ġfive Ġcategories , Ġthe Ġoutput Ġof Ġthe Ġapproach Ġis Ġa Ġ5 - dimensional Ġvector Ġwith Ġthe Ġestimated Ġprevalence Ġof Ġthe Ġcategories . </s> The Ġevaluation Ġmeasure Ġfor Ġthe Ġproblem Ġis Ġthe ĠEarth ĠMo vers ĠDistance Ġ( EM D ) ĠB IB REF 18 Ġ. ĠE MD Ġis Ġa Ġmeasure Ġof Ġerror , Ġhence Ġlower Ġvalues Ġare Ġbetter . ĠIt Ġassumes Ġordered Ġcategories , Ġwhich Ġin Ġour Ġproblem Ġis Ġnaturally Ġdefined . ĠFurther Ġassuming Ġthat Ġthe Ġdistance Ġof Ġconsecutive Ġcategories Ġ( e . g ., ĠPositive Ġand ĠVery Pos itive ) Ġis Ġ1 , Ġthe Ġmeasure Ġis Ġcalculated Ġby : ĠIN LINE FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġnumber Ġof Ġcategories Ġ( five Ġin Ġour Ġcase ) Ġand ĠIN LINE FORM 1 Ġand ĠIN LINE FORM 2 Ġare Ġthe Ġtrue Ġand Ġpredicted Ġprevalence Ġrespectively ĠB IB REF 19 Ġ. </s> Results ĠTable ĠT AB REF 13 Ġpresents Ġthe Ġresults Ġof Ġaugment ing Ġthe Ġfeature Ġset Ġwith Ġthe Ġproposed Ġfeatures . ĠWe Ġuse ĠLog istic ĠReg ression Ġas Ġa Ġbase Ġclass ifier Ġfor Ġthe Ġclassify Ġand Ġcount Ġapproach . ĠNotice Ġthe Ġpositive Ġimpact Ġof Ġthe Ġfeatures Ġin Ġthe Ġperformance Ġin Ġthe Ġtask . ĠAdding Ġthe Ġfeatures Ġderived Ġfrom Ġclust ering Ġthe Ġembed d ings Ġconsistently Ġimproves Ġthe Ġperformance . ĠInterestingly , Ġthe Ġbest Ġperformance Ġ( ĠIN LINE FORM 0 Ġ) Ġis Ġachieved Ġusing Ġthe Ġout - of - domain Ġvectors , Ġas Ġin Ġthe ĠN ER Ġclassification Ġtask . ĠAlso , Ġnotice Ġhow Ġthe Ġapproach Ġimproves Ġover Ġthe Ġstate - of - the - art Ġperformance Ġin Ġthe Ġchallenge Ġ( ĠIN LINE FORM 1 Ġ) ĠB IB REF 13 Ġ, Ġheld Ġby Ġthe Ġmethod Ġof ĠB IB REF 20 Ġ. ĠThe Ġimprovement Ġover Ġthe Ġmethod Ġof ĠB IB REF 20 Ġhowever , Ġdoes Ġnot Ġnecessarily Ġmean Ġthat Ġclassify Ġand Ġcount Ġperforms Ġbetter Ġin Ġthe Ġtask . ĠIt Ġimplies Ġthat Ġthe Ġfeature Ġset Ġwe Ġused Ġis Ġricher , Ġthat Ġin Ġturn Ġhighlights Ġthe Ġvalue Ġof Ġrobust Ġfeature Ġextraction Ġmechanisms Ġwhich Ġis Ġthe Ġsubject Ġof Ġthis Ġpaper . </s> Conclusion </s> We Ġhave Ġshown Ġempir ically Ġthe Ġeffectiveness Ġof Ġincorporating Ġcluster Ġmembership Ġfeatures Ġin Ġthe Ġfeature Ġextraction Ġpipeline Ġof ĠNamed - Entity Ġrecognition , Ġsentiment Ġclassification Ġand Ġquant ification Ġtasks . ĠOur Ġresults Ġstrongly Ġsuggest Ġthat Ġincorporating Ġcluster Ġmembership Ġfeatures Ġbenefit Ġthe Ġperformance Ġin Ġthe Ġtasks . ĠThe Ġfact Ġthat Ġthe Ġperformance Ġimprovements Ġare Ġconsistent Ġin Ġthe Ġfour Ġtasks Ġwe Ġinvestigated , Ġfurther Ġhighlights Ġtheir Ġusefulness , Ġboth Ġfor Ġpractitioners Ġand Ġresearchers . </s> Although Ġour Ġstudy Ġdoes Ġnot Ġidentify Ġa Ġclear Ġwinner Ġwith Ġrespect Ġto Ġthe Ġtype Ġof Ġword Ġvectors Ġ( skip gram , Ġc bow , Ġor ĠGl o Ve ), Ġour Ġfindings Ġsuggest Ġthat Ġone Ġshould Ġfirst Ġtry Ġskip - gram Ġembed d ings Ġof Ġlow Ġdimension ality Ġ( ĠIN LINE FORM 0 Ġ) Ġand Ġhigh Ġnumber Ġof Ġclusters Ġ( e . g ., ĠIN LINE FORM 1 Ġ) Ġas Ġthe Ġresults Ġobtained Ġusing Ġthese Ġsettings Ġare Ġconsistently Ġcompetitive . ĠOur Ġresults Ġalso Ġsuggest Ġthat Ġusing Ġout - of - domain Ġdata , Ġlike ĠWikipedia Ġarticles Ġin Ġthis Ġcase , Ġto Ġconstruct Ġthe Ġword Ġembed d ings Ġis Ġa Ġgood Ġpractice , Ġas Ġthe Ġresults Ġwe Ġobtained Ġwith Ġthese Ġvectors Ġare Ġalso Ġcompetitive . ĠThe Ġpositive Ġof Ġout - of - domain Ġembed d ings Ġand Ġtheir Ġcombination Ġwith Ġin - domain Ġones Ġremains Ġto Ġbe Ġfurther Ġstudied .
Introduction Many research attempts have proposed novel features that improve the performance of learning algorithms in particular tasks. Such features are often motivated by domain knowledge or manual labor. Although useful and often state-of-the-art, adapting such solutions on NLP systems across tasks can be tricky and time-consuming BIBREF0 . Therefore, simple yet general and powerful methods that perform well across several datasets are valuable BIBREF1 . An approach that has become extremely popular lately in NLP tasks, is to train word embeddings in an unsupervised way. These embeddings are dense vectors that project words or short text spans like phrases in a vector space where dimensions are supposed to capture text properties. Such embeddings can then be used either as features with off-the-shelf algorithms like Support Vector Machines, or to initialize deep learning systems BIBREF2 . However, as shown in BIBREF3 linear architectures perform better in high-dimensional discrete spaces compared to continuous ones. The latter is probably the main reason of the high performance of the vector space model BIBREF4 in tasks like text classification with linear models like SVMs. Using linear algorithms, while taking advantage of the expressiveness of text embeddings is the focus of this work. In this paper, we explore a hybrid approach, that uses text embeddings as a proxy to create features. Motivated by the argument that text embeddings manage to encode the semantics of text, we explore how clustering text embeddings can impact the performance of different NLP tasks. Although such an approach has been used in different studies during feature engineering, the selection of word vectors and the number of clusters remain a trial-end-error procedure. In this work we present an empirical evaluation across diverse tasks to verify whether and when such features are useful. Word clusters have been used as features in various tasks like Part-of-Speech tagging and NER. Owoputi et al. Owoputi13 use Brown clusters BIBREF5 in a POS tagger showing that this type of features carry rich lexical knowledge as they can substitute lexical resources like gazetteers. Kiritchenko et al. KiritchenkoZM14 discusses their use on sentiment classification while Hee et al. HeeLH16 incorporate them in the task of irony detection in Twitter. Ritter et al. Ritter2011 inject also word clusters in a NER tagger. While these works show that word clusters are beneficial no clear guidelines can be concluded of how and when to use them. In this work, we empirically demonstrate that using different types of embeddings on three NLP tasks with twitter data we manage to achieve better or near to the state-of-the art performance on three NLP tasks: (i) Named Entity Recognition (NER) segmentation, (ii) NER classification, (iii) fine-grained sentiment analysis and (iv) fine-grained sentiment quantification. For each of the three tasks, we achieve higher performance than without using features which indicates the effectiveness of the cluster membership features. Importantly, our evaluation compared to previous work BIBREF6 who focus on old and well studied datasets uses recent and challenging datasets composed by tweets. The obtained results across all the tasks permits us to reveal important aspects of the use of word clusters and therefore provide guidelines. Although our obtained scores are state-of-the-art, our analysis reveals that the performance in such tasks is far from perfect and, hence, identifies that there is still much space for improvement and future work. Word Clusters Word embeddings associate words with dense, low-dimensional vectors. Recently, several models have been proposed in order to obtain these embeddings. Among others, the skipgram (skipgram) model with negative sampling BIBREF7 , the continuous bag-of-words (cbow) model BIBREF7 and Glove (glove) BIBREF8 have been shown to be effective. Training those models requires no annotated data and can be done using big amounts of text. Such a model can be seen as a function INLINEFORM0 that projects a word INLINEFORM1 in a INLINEFORM2 -dimensional space: INLINEFORM3 , where INLINEFORM4 is predefined. Here, we focus on applications using data from Twitter, which pose several difficulties due to being particularly short, using creative vocabulary, abbreviations and slang. For all the tasks in our experimental study, we use 36 millions English tweets collected between August and September 2017. A pre-processing step has been applied to replace URLs with a placeholder and to pad punctuation. The final vocabulary size was around 1.6 millions words. Additionally to the in-domain corpus we collected, we use GloVe vectors trained on Wikipedia articles in order to investigate the impact of out-of-domain word-vectors. We cluster the embeddings with INLINEFORM0 -Means. The k-means clusters are initialized using “k-means++” as proposed in BIBREF9 , while the algorithm is run for 300 iterations. We try different values for INLINEFORM1 . For each INLINEFORM2 , we repeat the clustering experiment with different seed initialization for 10 times and we select the clustering result that minimizes the cluster inertia. Experimental Evaluation We evaluate the proposed approach for augmenting the feature space in four tasks: (i) NER segmentation, (ii) NER classification, (iii) fine-grained sentiment classification and (iv) fine-grained sentiment quantification. The next sections present the evaluation settings we used. For each of the tasks, we use the designated training sets to train the learning algorithms, and we report the scores of the evaluation measures used in the respective test parts. Named-Entity Recognition in Twitter NER concerns the classification of textual segments in a predefined set of categories, like persons, organization and locations. We use the data of the last competition in NER for Twitter which released as a part of the 2nd Workshop on Noisy User-generated Text BIBREF10 . More specifically, the organizers provided annotated tweets with 10 named-entity types (person, movie, sportsteam, product etc.) and the task comprised two sub-tasks: 1) the detection of entity bounds and 2) the classification of an entity into one of the 10 types. The evaluation measure for both sub-tasks is the F INLINEFORM0 measure. The following is an example of a tweet which contains two named entities. Note that named entities may span several words in the text: INLINEFORM0 tonite ... 90 's music .. oldskool night wiith INLINEFORM1 Our model for solving the task is a learning to search approach. More specifically we follow BIBREF11 which has been ranked 2nd among 10 participants in the aforementioned competition BIBREF10 . The model uses handcrafted features like n-grams, part-of-speech tags, capitalization and membership in gazetteers. The algorithm used belongs to the family of learning to search for structured prediction tasks BIBREF12 . These methods decompose the problem in a search space with states, actions and policies and then learn a hypothesis controlling a policy over the state-action space. The BIO encoding is used for attributing the corresponding labels to the tokens where B-type is used for the first token of the entity, I-type for inside tokens in case of multi-term entities and O for non entity tokens. Tables TABREF6 and TABREF7 present the results for the different number of clusters across the three vector models used to induce the clusters. For all the experiments we keep the same parametrization for the learning algorithm and we present the performance of each run on the official test set. Regarding the segmentation task we notice that adding word clusters as features improve the performance of the best model up to 1.1 F-score points while it boosts performance in the majority of cases. In only one case, for glove INLINEFORM0 vectors, there is a drop across all number of clusters used. As for the number of clusters, the best results are generally obtained between 250 and 1000 classes for all word vector models. These dimensions seem to be sufficient for the three-class sub-task that we deal with. The different models of word vectors perform similarly and thus one cannot privilege a certain type of word vectors. Interestingly, the clusters learned on the Wikipedia GloVe vectors offer competitive performance with respect to the in-domain word vectors used for the other cases showing that one can rely to out-of-domain data for constructing such representations. Concerning the classification task (Table TABREF7 ) we generally observe a drop in the performance of the tagger as we deal with 10 classes. This essentially corresponds to a multi-class problem with 21 classes: one for the non-entity type and two classes for each entity type. In this setting we notice that the best results are obtained in most cases for higher number of classes (1000 or 2000) possibly due to a better discriminatory power in higher dimensions. Note also, that in some cases the addition of word cluster features does not necessarily improve the performance. Contrary, it may degrade it as it is evident in the case of glove INLINEFORM0 word clusters. Like in the case of segmentation we do not observe a word vector model that clearly outperforms the rest. Finally, we note the same competitive performance of the Wikipedia word clusters and notably for the glove INLINEFORM1 clusters which obtain the best F1-score. Fine-grained Sentiment Analysis The task of fine grained sentiment classification consists in predicting the sentiment of an input text according to a five point scale (sentiment INLINEFORM0 {VeryNegative, Negative, Neutral, Positive, VeryPositive}). We use the setting of task 4 of SemEval2016 “Sentiment Analysis in Twitter” and the dataset released by the organizers for subtask 4 BIBREF13 . In total, the training (resp. test) data consist of 9,070 (resp. 20,632) tweets. The evaluation measure selected in BIBREF13 for the task in the macro-averaged Mean Absolute Error (MAE INLINEFORM0 ). It is a measure of error, hence lower values are better. The measure's goal is to take into account the order of the classes when penalizing the decision of a classifier. For instance, misclassifying a very negative example as very positive is a bigger mistake than classifying it as negative or neutral. Penalizing a classifier according to how far the predictions are from the true class is captured by MAE INLINEFORM1 BIBREF14 . Also, the advantage of using the macro- version instead of the standard version of the measure is the robustness against the class imbalance in the data. Learning algorithm To demonstrate the efficiency of cluster membership features we rely on the system of BIBREF15 which was ranked 1st among 11 participants and uses a Logistic Regression as a learning algorithm. We follow the same feature extraction steps which consist of extracting n-gram and character n-gram features, part-of-speech counts as well as sentiment scores using standard sentiment lexicons such as the Bing Liu's BIBREF16 and the MPQA lexicons BIBREF17 . For the full description, we refer the interested reader to BIBREF15 . To evaluate the performance of the proposed feature augmentation technique, we present in Table TABREF10 the macro-averaged Mean Absolute Error scores for different settings on the official test set of BIBREF13 . First, notice that the best score in the test data is achieved using cluster membership features, where the word embeddings are trained using the skipgram model. The achieved score improves the state-of-the art on the dataset, which to the best of our knowledge was by BIBREF15 . Also, note that the score on the test data improves for each type of embeddings used, which means that augmenting the feature space using cluster membership features helps the sentiment classification task. Note, also, that using the clusters produced by the out-of-domain embeddings trained on wikipedia that were released as part of BIBREF8 performs surprisingly well. One might have expected their addition to hurt the performance. However, their value probably stems from the sheer amount of data used for their training as well as the relatively simple type of words (like awesome, terrible) which are discriminative for this task. Lastly, note that in each of the settings, the best results are achieved when the number of clusters is within INLINEFORM0 as in the NER tasks. Comparing the performance across the different embeddings, one cannot claim that a particular embedding performs better. It is evident though that augmenting the feature space with feature derived using the proposed method, preferably with in-domain data, helps the classification performance and reduces MAE INLINEFORM1 . From the results of Table TABREF10 it is clear that the addition of the cluster membership features improves the sentiment classification performance. To better understand though why these clusters help, we manually examined a sample of the words associated with the clusters. To improve the eligibility of those results we first removed the hashtags and we filter the results using an English vocabulary. In Table TABREF11 we present sample words from two of the most characteristic clusters with respect to the task of sentiment classification. Notice how words with positive and negative meanings are put in the respective clusters. Fine-Grained Sentiment Quantification Quantification is the problem of estimating the prevalence of a class in a dataset. While classification concerns assigning a category to a single instance, like labeling a tweet with the sentiment it conveys, the goal of quantification is, given a set of instances, to estimate the relative frequency of single class. Therefore, sentiment quantification tries to answer questions like “Given a set of tweets about the new iPhone, what is the fraction of VeryPositive ones?”. In the rest, we show the effect of the features derived from the word embeddings clusters in the fine-grained classification problem, which was also part of the SemEval-2016 “Sentiment Analysis in Twitter” task BIBREF13 . Learning Algorithm To perform the quantification task, we rely on a classify and count approach, which was shown effective in a related binary quantification problem BIBREF15 . The idea is that given a set of instances on a particular subject, one first classifies the instances and then aggregates the counts. To this end, we use the same feature representation steps and data with the ones used for fine grained classification (Section 3.2). Note that the data of the task are associated with subjects (described in full detail at BIBREF13 ), and, hence, quantification is performed for the tweets of a subject. For each of the five categories, the output of the approach is a 5-dimensional vector with the estimated prevalence of the categories. The evaluation measure for the problem is the Earth Movers Distance (EMD) BIBREF18 . EMD is a measure of error, hence lower values are better. It assumes ordered categories, which in our problem is naturally defined. Further assuming that the distance of consecutive categories (e.g., Positive and VeryPositive) is 1, the measure is calculated by: INLINEFORM0 where INLINEFORM0 is number of categories (five in our case) and INLINEFORM1 and INLINEFORM2 are the true and predicted prevalence respectively BIBREF19 . Results Table TABREF13 presents the results of augmenting the feature set with the proposed features. We use Logistic Regression as a base classifier for the classify and count approach. Notice the positive impact of the features in the performance in the task. Adding the features derived from clustering the embeddings consistently improves the performance. Interestingly, the best performance ( INLINEFORM0 ) is achieved using the out-of-domain vectors, as in the NER classification task. Also, notice how the approach improves over the state-of-the-art performance in the challenge ( INLINEFORM1 ) BIBREF13 , held by the method of BIBREF20 . The improvement over the method of BIBREF20 however, does not necessarily mean that classify and count performs better in the task. It implies that the feature set we used is richer, that in turn highlights the value of robust feature extraction mechanisms which is the subject of this paper. Conclusion We have shown empirically the effectiveness of incorporating cluster membership features in the feature extraction pipeline of Named-Entity recognition, sentiment classification and quantification tasks. Our results strongly suggest that incorporating cluster membership features benefit the performance in the tasks. The fact that the performance improvements are consistent in the four tasks we investigated, further highlights their usefulness, both for practitioners and researchers. Although our study does not identify a clear winner with respect to the type of word vectors (skipgram, cbow, or GloVe), our findings suggest that one should first try skip-gram embeddings of low dimensionality ( INLINEFORM0 ) and high number of clusters (e.g., INLINEFORM1 ) as the results obtained using these settings are consistently competitive. Our results also suggest that using out-of-domain data, like Wikipedia articles in this case, to construct the word embeddings is a good practice, as the results we obtained with these vectors are also competitive. The positive of out-of-domain embeddings and their combination with in-domain ones remains to be further studied.
Which other hyperparameters, other than number of clusters are typically evaluated in this type of research?
a4a1fcef760b133e9aa876ac28145ad98a609927
Which Ġother Ġhyper param eters , Ġother Ġthan Ġnumber Ġof Ġclusters Ġare Ġtypically Ġevaluated Ġin Ġthis Ġtype Ġof Ġresearch ?
selection Ġof Ġword Ġvectors
<s> Which Ġother Ġhyper param eters , Ġother Ġthan Ġnumber Ġof Ġclusters Ġare Ġtypically Ġevaluated Ġin Ġthis Ġtype Ġof Ġresearch ? </s> Introduction </s> Many Ġresearch Ġattempts Ġhave Ġproposed Ġnovel Ġfeatures Ġthat Ġimprove Ġthe Ġperformance Ġof Ġlearning Ġalgorithms Ġin Ġparticular Ġtasks . ĠSuch Ġfeatures Ġare Ġoften Ġmotivated Ġby Ġdomain Ġknowledge Ġor Ġmanual Ġlabor . ĠAlthough Ġuseful Ġand Ġoften Ġstate - of - the - art , Ġadapting Ġsuch Ġsolutions Ġon ĠN LP Ġsystems Ġacross Ġtasks Ġcan Ġbe Ġtricky Ġand Ġtime - consuming ĠB IB REF 0 Ġ. ĠTherefore , Ġsimple Ġyet Ġgeneral Ġand Ġpowerful Ġmethods Ġthat Ġperform Ġwell Ġacross Ġseveral Ġdatasets Ġare Ġvaluable ĠB IB REF 1 Ġ. </s> An Ġapproach Ġthat Ġhas Ġbecome Ġextremely Ġpopular Ġlately Ġin ĠN LP Ġtasks , Ġis Ġto Ġtrain Ġword Ġembed d ings Ġin Ġan Ġun super vised Ġway . ĠThese Ġembed d ings Ġare Ġdense Ġvectors Ġthat Ġproject Ġwords Ġor Ġshort Ġtext Ġspans Ġlike Ġphrases Ġin Ġa Ġvector Ġspace Ġwhere Ġdimensions Ġare Ġsupposed Ġto Ġcapture Ġtext Ġproperties . ĠSuch Ġembed d ings Ġcan Ġthen Ġbe Ġused Ġeither Ġas Ġfeatures Ġwith Ġoff - the - she lf Ġalgorithms Ġlike ĠSupport ĠVector ĠMachines , Ġor Ġto Ġinitialize Ġdeep Ġlearning Ġsystems ĠB IB REF 2 Ġ. ĠHowever , Ġas Ġshown Ġin ĠB IB REF 3 Ġlinear Ġarchitectures Ġperform Ġbetter Ġin Ġhigh - dimensional Ġdiscrete Ġspaces Ġcompared Ġto Ġcontinuous Ġones . ĠThe Ġlatter Ġis Ġprobably Ġthe Ġmain Ġreason Ġof Ġthe Ġhigh Ġperformance Ġof Ġthe Ġvector Ġspace Ġmodel ĠB IB REF 4 Ġin Ġtasks Ġlike Ġtext Ġclassification Ġwith Ġlinear Ġmodels Ġlike ĠSV Ms . ĠUsing Ġlinear Ġalgorithms , Ġwhile Ġtaking Ġadvantage Ġof Ġthe Ġexpress iveness Ġof Ġtext Ġembed d ings Ġis Ġthe Ġfocus Ġof Ġthis Ġwork . </s> In Ġthis Ġpaper , Ġwe Ġexplore Ġa Ġhybrid Ġapproach , Ġthat Ġuses Ġtext Ġembed d ings Ġas Ġa Ġproxy Ġto Ġcreate Ġfeatures . ĠMot ivated Ġby Ġthe Ġargument Ġthat Ġtext Ġembed d ings Ġmanage Ġto Ġencode Ġthe Ġsemantics Ġof Ġtext , Ġwe Ġexplore Ġhow Ġclust ering Ġtext Ġembed d ings Ġcan Ġimpact Ġthe Ġperformance Ġof Ġdifferent ĠN LP Ġtasks . ĠAlthough Ġsuch Ġan Ġapproach Ġhas Ġbeen Ġused Ġin Ġdifferent Ġstudies Ġduring Ġfeature Ġengineering , Ġthe Ġselection Ġof Ġword Ġvectors Ġand Ġthe Ġnumber Ġof Ġclusters Ġremain Ġa Ġtrial - end - error Ġprocedure . ĠIn Ġthis Ġwork Ġwe Ġpresent Ġan Ġempirical Ġevaluation Ġacross Ġdiverse Ġtasks Ġto Ġverify Ġwhether Ġand Ġwhen Ġsuch Ġfeatures Ġare Ġuseful . </s> Word Ġclusters Ġhave Ġbeen Ġused Ġas Ġfeatures Ġin Ġvarious Ġtasks Ġlike ĠPart - of - Spe ech Ġtagging Ġand ĠN ER . ĠOw op uti Ġet Ġal . ĠOw op uti 13 Ġuse ĠBrown Ġclusters ĠB IB REF 5 Ġin Ġa ĠPOS Ġtag ger Ġshowing Ġthat Ġthis Ġtype Ġof Ġfeatures Ġcarry Ġrich Ġlex ical Ġknowledge Ġas Ġthey Ġcan Ġsubstitute Ġlex ical Ġresources Ġlike Ġg az ette ers . ĠK irit chenko Ġet Ġal . ĠK irit chenko Z M 14 Ġdiscusses Ġtheir Ġuse Ġon Ġsentiment Ġclassification Ġwhile ĠHe e Ġet Ġal . ĠHe e L H 16 Ġincorporate Ġthem Ġin Ġthe Ġtask Ġof Ġirony Ġdetection Ġin ĠTwitter . ĠR itter Ġet Ġal . ĠR itter 2011 Ġinject Ġalso Ġword Ġclusters Ġin Ġa ĠN ER Ġtag ger . ĠWhile Ġthese Ġworks Ġshow Ġthat Ġword Ġclusters Ġare Ġbeneficial Ġno Ġclear Ġguidelines Ġcan Ġbe Ġconcluded Ġof Ġhow Ġand Ġwhen Ġto Ġuse Ġthem . </s> In Ġthis Ġwork , Ġwe Ġempir ically Ġdemonstrate Ġthat Ġusing Ġdifferent Ġtypes Ġof Ġembed d ings Ġon Ġthree ĠN LP Ġtasks Ġwith Ġtwitter Ġdata Ġwe Ġmanage Ġto Ġachieve Ġbetter Ġor Ġnear Ġto Ġthe Ġstate - of - the Ġart Ġperformance Ġon Ġthree ĠN LP Ġtasks : Ġ( i ) ĠNamed ĠEntity ĠRecogn ition Ġ( NER ) Ġsegment ation , Ġ( ii ) ĠN ER Ġclassification , Ġ( iii ) Ġfine - gr ained Ġsentiment Ġanalysis Ġand Ġ( iv ) Ġfine - gr ained Ġsentiment Ġquant ification . ĠFor Ġeach Ġof Ġthe Ġthree Ġtasks , Ġwe Ġachieve Ġhigher Ġperformance Ġthan Ġwithout Ġusing Ġfeatures Ġwhich Ġindicates Ġthe Ġeffectiveness Ġof Ġthe Ġcluster Ġmembership Ġfeatures . ĠImport antly , Ġour Ġevaluation Ġcompared Ġto Ġprevious Ġwork ĠB IB REF 6 Ġwho Ġfocus Ġon Ġold Ġand Ġwell Ġstudied Ġdatasets Ġuses Ġrecent Ġand Ġchallenging Ġdatasets Ġcomposed Ġby Ġtweets . ĠThe Ġobtained Ġresults Ġacross Ġall Ġthe Ġtasks Ġpermits Ġus Ġto Ġreveal Ġimportant Ġaspects Ġof Ġthe Ġuse Ġof Ġword Ġclusters Ġand Ġtherefore Ġprovide Ġguidelines . ĠAlthough Ġour Ġobtained Ġscores Ġare Ġstate - of - the - art , Ġour Ġanalysis Ġreveals Ġthat Ġthe Ġperformance Ġin Ġsuch Ġtasks Ġis Ġfar Ġfrom Ġperfect Ġand , Ġhence , Ġidentifies Ġthat Ġthere Ġis Ġstill Ġmuch Ġspace Ġfor Ġimprovement Ġand Ġfuture Ġwork . </s> Word ĠCl usters </s> Word Ġembed d ings Ġassociate Ġwords Ġwith Ġdense , Ġlow - dimensional Ġvectors . ĠRecently , Ġseveral Ġmodels Ġhave Ġbeen Ġproposed Ġin Ġorder Ġto Ġobtain Ġthese Ġembed d ings . ĠAmong Ġothers , Ġthe Ġskip gram Ġ( skip gram ) Ġmodel Ġwith Ġnegative Ġsampling ĠB IB REF 7 Ġ, Ġthe Ġcontinuous Ġbag - of - words Ġ( c bow ) Ġmodel ĠB IB REF 7 Ġand ĠGl ove Ġ( gl ove ) ĠB IB REF 8 Ġhave Ġbeen Ġshown Ġto Ġbe Ġeffective . ĠTraining Ġthose Ġmodels Ġrequires Ġno Ġannot ated Ġdata Ġand Ġcan Ġbe Ġdone Ġusing Ġbig Ġamounts Ġof Ġtext . ĠSuch Ġa Ġmodel Ġcan Ġbe Ġseen Ġas Ġa Ġfunction ĠIN LINE FORM 0 Ġthat Ġprojects Ġa Ġword ĠIN LINE FORM 1 Ġin Ġa ĠIN LINE FORM 2 Ġ- dimensional Ġspace : ĠIN LINE FORM 3 Ġ, Ġwhere ĠIN LINE FORM 4 Ġis Ġpred efined . ĠHere , Ġwe Ġfocus Ġon Ġapplications Ġusing Ġdata Ġfrom ĠTwitter , Ġwhich Ġpose Ġseveral Ġdifficulties Ġdue Ġto Ġbeing Ġparticularly Ġshort , Ġusing Ġcreative Ġvocabulary , Ġabbrevi ations Ġand Ġslang . </s> For Ġall Ġthe Ġtasks Ġin Ġour Ġexperimental Ġstudy , Ġwe Ġuse Ġ36 Ġmillions ĠEnglish Ġtweets Ġcollected Ġbetween ĠAugust Ġand ĠSeptember Ġ2017 . ĠA Ġpre - processing Ġstep Ġhas Ġbeen Ġapplied Ġto Ġreplace ĠURLs Ġwith Ġa Ġplaceholder Ġand Ġto Ġpad Ġpunct uation . ĠThe Ġfinal Ġvocabulary Ġsize Ġwas Ġaround Ġ1 . 6 Ġmillions Ġwords . ĠAdditionally Ġto Ġthe Ġin - domain Ġcorpus Ġwe Ġcollected , Ġwe Ġuse ĠGl o Ve Ġvectors Ġtrained Ġon ĠWikipedia Ġarticles Ġin Ġorder Ġto Ġinvestigate Ġthe Ġimpact Ġof Ġout - of - domain Ġword - ve ctors . </s> We Ġcluster Ġthe Ġembed d ings Ġwith ĠIN LINE FORM 0 Ġ- Me ans . ĠThe Ġk - me ans Ġclusters Ġare Ġinitialized Ġusing ĠâĢ ľ k - me ans ++ âĢ Ŀ Ġas Ġproposed Ġin ĠB IB REF 9 Ġ, Ġwhile Ġthe Ġalgorithm Ġis Ġrun Ġfor Ġ300 Ġiterations . ĠWe Ġtry Ġdifferent Ġvalues Ġfor ĠIN LINE FORM 1 Ġ. ĠFor Ġeach ĠIN LINE FORM 2 Ġ, Ġwe Ġrepeat Ġthe Ġclust ering Ġexperiment Ġwith Ġdifferent Ġseed Ġinitialization Ġfor Ġ10 Ġtimes Ġand Ġwe Ġselect Ġthe Ġclust ering Ġresult Ġthat Ġminim izes Ġthe Ġcluster Ġinertia . </s> Exper imental ĠEvaluation </s> We Ġevaluate Ġthe Ġproposed Ġapproach Ġfor Ġaugment ing Ġthe Ġfeature Ġspace Ġin Ġfour Ġtasks : Ġ( i ) ĠN ER Ġsegment ation , Ġ( ii ) ĠN ER Ġclassification , Ġ( iii ) Ġfine - gr ained Ġsentiment Ġclassification Ġand Ġ( iv ) Ġfine - gr ained Ġsentiment Ġquant ification . ĠThe Ġnext Ġsections Ġpresent Ġthe Ġevaluation Ġsettings Ġwe Ġused . ĠFor Ġeach Ġof Ġthe Ġtasks , Ġwe Ġuse Ġthe Ġdesignated Ġtraining Ġsets Ġto Ġtrain Ġthe Ġlearning Ġalgorithms , Ġand Ġwe Ġreport Ġthe Ġscores Ġof Ġthe Ġevaluation Ġmeasures Ġused Ġin Ġthe Ġrespective Ġtest Ġparts . </s> N amed - Entity ĠRecogn ition Ġin ĠTwitter </s> NER Ġconcerns Ġthe Ġclassification Ġof Ġtextual Ġsegments Ġin Ġa Ġpred efined Ġset Ġof Ġcategories , Ġlike Ġpersons , Ġorganization Ġand Ġlocations . ĠWe Ġuse Ġthe Ġdata Ġof Ġthe Ġlast Ġcompetition Ġin ĠN ER Ġfor ĠTwitter Ġwhich Ġreleased Ġas Ġa Ġpart Ġof Ġthe Ġ2 nd ĠWorkshop Ġon ĠNo isy ĠUser - generated ĠText ĠB IB REF 10 Ġ. ĠMore Ġspecifically , Ġthe Ġorganizers Ġprovided Ġannot ated Ġtweets Ġwith Ġ10 Ġnamed - entity Ġtypes Ġ( person , Ġmovie , Ġsport steam , Ġproduct Ġetc .) Ġand Ġthe Ġtask Ġcomprised Ġtwo Ġsub - t asks : Ġ1 ) Ġthe Ġdetection Ġof Ġentity Ġbounds Ġand Ġ2 ) Ġthe Ġclassification Ġof Ġan Ġentity Ġinto Ġone Ġof Ġthe Ġ10 Ġtypes . ĠThe Ġevaluation Ġmeasure Ġfor Ġboth Ġsub - t asks Ġis Ġthe ĠF ĠIN LINE FORM 0 Ġmeasure . </s> The Ġfollowing Ġis Ġan Ġexample Ġof Ġa Ġtweet Ġwhich Ġcontains Ġtwo Ġnamed Ġentities . ĠNote Ġthat Ġnamed Ġentities Ġmay Ġspan Ġseveral Ġwords Ġin Ġthe Ġtext : </s> IN LINE FORM 0 Ġton ite Ġ... Ġ90 Ġ' s Ġmusic Ġ.. Ġold sk ool Ġnight Ġwi ith ĠIN LINE FORM 1 </s> Our Ġmodel Ġfor Ġsolving Ġthe Ġtask Ġis Ġa Ġlearning Ġto Ġsearch Ġapproach . ĠMore Ġspecifically Ġwe Ġfollow ĠB IB REF 11 Ġwhich Ġhas Ġbeen Ġranked Ġ2 nd Ġamong Ġ10 Ġparticipants Ġin Ġthe Ġaforementioned Ġcompetition ĠB IB REF 10 Ġ. ĠThe Ġmodel Ġuses Ġhand crafted Ġfeatures Ġlike Ġn - gram s , Ġpart - of - speech Ġtags , Ġcapital ization Ġand Ġmembership Ġin Ġg az ette ers . ĠThe Ġalgorithm Ġused Ġbelongs Ġto Ġthe Ġfamily Ġof Ġlearning Ġto Ġsearch Ġfor Ġstructured Ġprediction Ġtasks ĠB IB REF 12 Ġ. ĠThese Ġmethods Ġdecom pose Ġthe Ġproblem Ġin Ġa Ġsearch Ġspace Ġwith Ġstates , Ġactions Ġand Ġpolicies Ġand Ġthen Ġlearn Ġa Ġhypothesis Ġcontrolling Ġa Ġpolicy Ġover Ġthe Ġstate - action Ġspace . ĠThe ĠB IO Ġencoding Ġis Ġused Ġfor Ġattribut ing Ġthe Ġcorresponding Ġlabels Ġto Ġthe Ġtokens Ġwhere ĠB - type Ġis Ġused Ġfor Ġthe Ġfirst Ġtoken Ġof Ġthe Ġentity , ĠI - type Ġfor Ġinside Ġtokens Ġin Ġcase Ġof Ġmulti - term Ġentities Ġand ĠO Ġfor Ġnon Ġentity Ġtokens . </s> T ables ĠT AB REF 6 Ġand ĠT AB REF 7 Ġpresent Ġthe Ġresults Ġfor Ġthe Ġdifferent Ġnumber Ġof Ġclusters Ġacross Ġthe Ġthree Ġvector Ġmodels Ġused Ġto Ġinduce Ġthe Ġclusters . ĠFor Ġall Ġthe Ġexperiments Ġwe Ġkeep Ġthe Ġsame Ġparam et riz ation Ġfor Ġthe Ġlearning Ġalgorithm Ġand Ġwe Ġpresent Ġthe Ġperformance Ġof Ġeach Ġrun Ġon Ġthe Ġofficial Ġtest Ġset . </s> Regarding Ġthe Ġsegment ation Ġtask Ġwe Ġnotice Ġthat Ġadding Ġword Ġclusters Ġas Ġfeatures Ġimprove Ġthe Ġperformance Ġof Ġthe Ġbest Ġmodel Ġup Ġto Ġ1 . 1 ĠF - score Ġpoints Ġwhile Ġit Ġboosts Ġperformance Ġin Ġthe Ġmajority Ġof Ġcases . ĠIn Ġonly Ġone Ġcase , Ġfor Ġglove ĠIN LINE FORM 0 Ġvectors , Ġthere Ġis Ġa Ġdrop Ġacross Ġall Ġnumber Ġof Ġclusters Ġused . </s> As Ġfor Ġthe Ġnumber Ġof Ġclusters , Ġthe Ġbest Ġresults Ġare Ġgenerally Ġobtained Ġbetween Ġ250 Ġand Ġ1000 Ġclasses Ġfor Ġall Ġword Ġvector Ġmodels . ĠThese Ġdimensions Ġseem Ġto Ġbe Ġsufficient Ġfor Ġthe Ġthree - class Ġsub - task Ġthat Ġwe Ġdeal Ġwith . ĠThe Ġdifferent Ġmodels Ġof Ġword Ġvectors Ġperform Ġsimilarly Ġand Ġthus Ġone Ġcannot Ġprivilege Ġa Ġcertain Ġtype Ġof Ġword Ġvectors . ĠInterestingly , Ġthe Ġclusters Ġlearned Ġon Ġthe ĠWikipedia ĠGl o Ve Ġvectors Ġoffer Ġcompetitive Ġperformance Ġwith Ġrespect Ġto Ġthe Ġin - domain Ġword Ġvectors Ġused Ġfor Ġthe Ġother Ġcases Ġshowing Ġthat Ġone Ġcan Ġrely Ġto Ġout - of - domain Ġdata Ġfor Ġconstructing Ġsuch Ġrepresentations . </s> Con cerning Ġthe Ġclassification Ġtask Ġ( Table ĠT AB REF 7 Ġ) Ġwe Ġgenerally Ġobserve Ġa Ġdrop Ġin Ġthe Ġperformance Ġof Ġthe Ġtag ger Ġas Ġwe Ġdeal Ġwith Ġ10 Ġclasses . ĠThis Ġessentially Ġcorresponds Ġto Ġa Ġmulti - class Ġproblem Ġwith Ġ21 Ġclasses : Ġone Ġfor Ġthe Ġnon - entity Ġtype Ġand Ġtwo Ġclasses Ġfor Ġeach Ġentity Ġtype . ĠIn Ġthis Ġsetting Ġwe Ġnotice Ġthat Ġthe Ġbest Ġresults Ġare Ġobtained Ġin Ġmost Ġcases Ġfor Ġhigher Ġnumber Ġof Ġclasses Ġ( 1000 Ġor Ġ2000 ) Ġpossibly Ġdue Ġto Ġa Ġbetter Ġdiscriminatory Ġpower Ġin Ġhigher Ġdimensions . ĠNote Ġalso , Ġthat Ġin Ġsome Ġcases Ġthe Ġaddition Ġof Ġword Ġcluster Ġfeatures Ġdoes Ġnot Ġnecessarily Ġimprove Ġthe Ġperformance . ĠContrary , Ġit Ġmay Ġdegrade Ġit Ġas Ġit Ġis Ġevident Ġin Ġthe Ġcase Ġof Ġglove ĠIN LINE FORM 0 Ġword Ġclusters . ĠLike Ġin Ġthe Ġcase Ġof Ġsegment ation Ġwe Ġdo Ġnot Ġobserve Ġa Ġword Ġvector Ġmodel Ġthat Ġclearly Ġoutper forms Ġthe Ġrest . ĠFinally , Ġwe Ġnote Ġthe Ġsame Ġcompetitive Ġperformance Ġof Ġthe ĠWikipedia Ġword Ġclusters Ġand Ġnotably Ġfor Ġthe Ġglove ĠIN LINE FORM 1 Ġclusters Ġwhich Ġobtain Ġthe Ġbest ĠF 1 - score . </s> Fine - gr ained ĠSent iment ĠAnalysis </s> The Ġtask Ġof Ġfine Ġgr ained Ġsentiment Ġclassification Ġconsists Ġin Ġpredicting Ġthe Ġsentiment Ġof Ġan Ġinput Ġtext Ġaccording Ġto Ġa Ġfive Ġpoint Ġscale Ġ( sent iment ĠIN LINE FORM 0 Ġ{ Very Neg ative , ĠNegative , ĠNeutral , ĠPositive , ĠVery Pos itive } ). ĠWe Ġuse Ġthe Ġsetting Ġof Ġtask Ġ4 Ġof ĠSem E val 2016 ĠâĢ ľ Sent iment ĠAnalysis Ġin ĠTwitter âĢ Ŀ Ġand Ġthe Ġdataset Ġreleased Ġby Ġthe Ġorganizers Ġfor Ġsubt ask Ġ4 ĠB IB REF 13 Ġ. </s> In Ġtotal , Ġthe Ġtraining Ġ( resp . Ġtest ) Ġdata Ġconsist Ġof Ġ9 , 070 Ġ( resp . Ġ20 , 6 32 ) Ġtweets . </s> The Ġevaluation Ġmeasure Ġselected Ġin ĠB IB REF 13 Ġfor Ġthe Ġtask Ġin Ġthe Ġmacro - aver aged ĠMean ĠAbsolute ĠError Ġ( MA E ĠIN LINE FORM 0 Ġ). ĠIt Ġis Ġa Ġmeasure Ġof Ġerror , Ġhence Ġlower Ġvalues Ġare Ġbetter . ĠThe Ġmeasure 's Ġgoal Ġis Ġto Ġtake Ġinto Ġaccount Ġthe Ġorder Ġof Ġthe Ġclasses Ġwhen Ġpenal izing Ġthe Ġdecision Ġof Ġa Ġclass ifier . ĠFor Ġinstance , Ġmis class ifying Ġa Ġvery Ġnegative Ġexample Ġas Ġvery Ġpositive Ġis Ġa Ġbigger Ġmistake Ġthan Ġclass ifying Ġit Ġas Ġnegative Ġor Ġneutral . ĠPenal izing Ġa Ġclass ifier Ġaccording Ġto Ġhow Ġfar Ġthe Ġpredictions Ġare Ġfrom Ġthe Ġtrue Ġclass Ġis Ġcaptured Ġby ĠMA E ĠIN LINE FORM 1 ĠB IB REF 14 Ġ. ĠAlso , Ġthe Ġadvantage Ġof Ġusing Ġthe Ġmacro - Ġversion Ġinstead Ġof Ġthe Ġstandard Ġversion Ġof Ġthe Ġmeasure Ġis Ġthe Ġrobust ness Ġagainst Ġthe Ġclass Ġimbalance Ġin Ġthe Ġdata . </s> Learning Ġalgorithm ĠTo Ġdemonstrate Ġthe Ġefficiency Ġof Ġcluster Ġmembership Ġfeatures Ġwe Ġrely Ġon Ġthe Ġsystem Ġof ĠB IB REF 15 Ġwhich Ġwas Ġranked Ġ1 st Ġamong Ġ11 Ġparticipants Ġand Ġuses Ġa ĠLog istic ĠReg ression Ġas Ġa Ġlearning Ġalgorithm . ĠWe Ġfollow Ġthe Ġsame Ġfeature Ġextraction Ġsteps Ġwhich Ġconsist Ġof Ġextracting Ġn - gram Ġand Ġcharacter Ġn - gram Ġfeatures , Ġpart - of - speech Ġcounts Ġas Ġwell Ġas Ġsentiment Ġscores Ġusing Ġstandard Ġsentiment Ġlex icons Ġsuch Ġas Ġthe ĠBing ĠLiu 's ĠB IB REF 16 Ġand Ġthe ĠMP Q A Ġlex icons ĠB IB REF 17 Ġ. ĠFor Ġthe Ġfull Ġdescription , Ġwe Ġrefer Ġthe Ġinterested Ġreader Ġto ĠB IB REF 15 Ġ. </s> To Ġevaluate Ġthe Ġperformance Ġof Ġthe Ġproposed Ġfeature Ġaug mentation Ġtechnique , Ġwe Ġpresent Ġin ĠTable ĠT AB REF 10 Ġthe Ġmacro - aver aged ĠMean ĠAbsolute ĠError Ġscores Ġfor Ġdifferent Ġsettings Ġon Ġthe Ġofficial Ġtest Ġset Ġof ĠB IB REF 13 Ġ. ĠFirst , Ġnotice Ġthat Ġthe Ġbest Ġscore Ġin Ġthe Ġtest Ġdata Ġis Ġachieved Ġusing Ġcluster Ġmembership Ġfeatures , Ġwhere Ġthe Ġword Ġembed d ings Ġare Ġtrained Ġusing Ġthe Ġskip gram Ġmodel . ĠThe Ġachieved Ġscore Ġimproves Ġthe Ġstate - of - the Ġart Ġon Ġthe Ġdataset , Ġwhich Ġto Ġthe Ġbest Ġof Ġour Ġknowledge Ġwas Ġby ĠB IB REF 15 Ġ. ĠAlso , Ġnote Ġthat Ġthe Ġscore Ġon Ġthe Ġtest Ġdata Ġimproves Ġfor Ġeach Ġtype Ġof Ġembed d ings Ġused , Ġwhich Ġmeans Ġthat Ġaugment ing Ġthe Ġfeature Ġspace Ġusing Ġcluster Ġmembership Ġfeatures Ġhelps Ġthe Ġsentiment Ġclassification Ġtask . </s> Note , Ġalso , Ġthat Ġusing Ġthe Ġclusters Ġproduced Ġby Ġthe Ġout - of - domain Ġembed d ings Ġtrained Ġon Ġwik ipedia Ġthat Ġwere Ġreleased Ġas Ġpart Ġof ĠB IB REF 8 Ġperforms Ġsurprisingly Ġwell . ĠOne Ġmight Ġhave Ġexpected Ġtheir Ġaddition Ġto Ġhurt Ġthe Ġperformance . ĠHowever , Ġtheir Ġvalue Ġprobably Ġstems Ġfrom Ġthe Ġsheer Ġamount Ġof Ġdata Ġused Ġfor Ġtheir Ġtraining Ġas Ġwell Ġas Ġthe Ġrelatively Ġsimple Ġtype Ġof Ġwords Ġ( like Ġawesome , Ġterrible ) Ġwhich Ġare Ġdiscrim in ative Ġfor Ġthis Ġtask . ĠLastly , Ġnote Ġthat Ġin Ġeach Ġof Ġthe Ġsettings , Ġthe Ġbest Ġresults Ġare Ġachieved Ġwhen Ġthe Ġnumber Ġof Ġclusters Ġis Ġwithin ĠIN LINE FORM 0 Ġas Ġin Ġthe ĠN ER Ġtasks . ĠComp aring Ġthe Ġperformance Ġacross Ġthe Ġdifferent Ġembed d ings , Ġone Ġcannot Ġclaim Ġthat Ġa Ġparticular Ġembed ding Ġperforms Ġbetter . ĠIt Ġis Ġevident Ġthough Ġthat Ġaugment ing Ġthe Ġfeature Ġspace Ġwith Ġfeature Ġderived Ġusing Ġthe Ġproposed Ġmethod , Ġpreferably Ġwith Ġin - domain Ġdata , Ġhelps Ġthe Ġclassification Ġperformance Ġand Ġreduces ĠMA E ĠIN LINE FORM 1 Ġ. </s> From Ġthe Ġresults Ġof ĠTable ĠT AB REF 10 Ġit Ġis Ġclear Ġthat Ġthe Ġaddition Ġof Ġthe Ġcluster Ġmembership Ġfeatures Ġimproves Ġthe Ġsentiment Ġclassification Ġperformance . ĠTo Ġbetter Ġunderstand Ġthough Ġwhy Ġthese Ġclusters Ġhelp , Ġwe Ġmanually Ġexamined Ġa Ġsample Ġof Ġthe Ġwords Ġassociated Ġwith Ġthe Ġclusters . ĠTo Ġimprove Ġthe Ġeligibility Ġof Ġthose Ġresults Ġwe Ġfirst Ġremoved Ġthe Ġhasht ags Ġand Ġwe Ġfilter Ġthe Ġresults Ġusing Ġan ĠEnglish Ġvocabulary . ĠIn ĠTable ĠT AB REF 11 Ġwe Ġpresent Ġsample Ġwords Ġfrom Ġtwo Ġof Ġthe Ġmost Ġcharacteristic Ġclusters Ġwith Ġrespect Ġto Ġthe Ġtask Ġof Ġsentiment Ġclassification . ĠNotice Ġhow Ġwords Ġwith Ġpositive Ġand Ġnegative Ġmeanings Ġare Ġput Ġin Ġthe Ġrespective Ġclusters . </s> Fine - Gr ained ĠSent iment ĠQuant ification </s> Quant ification Ġis Ġthe Ġproblem Ġof Ġestimating Ġthe Ġprevalence Ġof Ġa Ġclass Ġin Ġa Ġdataset . ĠWhile Ġclassification Ġconcerns Ġassigning Ġa Ġcategory Ġto Ġa Ġsingle Ġinstance , Ġlike Ġlabeling Ġa Ġtweet Ġwith Ġthe Ġsentiment Ġit Ġconve ys , Ġthe Ġgoal Ġof Ġquant ification Ġis , Ġgiven Ġa Ġset Ġof Ġinstances , Ġto Ġestimate Ġthe Ġrelative Ġfrequency Ġof Ġsingle Ġclass . ĠTherefore , Ġsentiment Ġquant ification Ġtries Ġto Ġanswer Ġquestions Ġlike ĠâĢ ľ Given Ġa Ġset Ġof Ġtweets Ġabout Ġthe Ġnew ĠiPhone , Ġwhat Ġis Ġthe Ġfraction Ġof ĠVery Pos itive Ġones ? âĢ Ŀ . ĠIn Ġthe Ġrest , Ġwe Ġshow Ġthe Ġeffect Ġof Ġthe Ġfeatures Ġderived Ġfrom Ġthe Ġword Ġembed d ings Ġclusters Ġin Ġthe Ġfine - gr ained Ġclassification Ġproblem , Ġwhich Ġwas Ġalso Ġpart Ġof Ġthe ĠSem E val - 2016 ĠâĢ ľ Sent iment ĠAnalysis Ġin ĠTwitter âĢ Ŀ Ġtask ĠB IB REF 13 Ġ. </s> Learning ĠAl gorithm ĠTo Ġperform Ġthe Ġquant ification Ġtask , Ġwe Ġrely Ġon Ġa Ġclassify Ġand Ġcount Ġapproach , Ġwhich Ġwas Ġshown Ġeffective Ġin Ġa Ġrelated Ġbinary Ġquant ification Ġproblem ĠB IB REF 15 Ġ. ĠThe Ġidea Ġis Ġthat Ġgiven Ġa Ġset Ġof Ġinstances Ġon Ġa Ġparticular Ġsubject , Ġone Ġfirst Ġclass ifies Ġthe Ġinstances Ġand Ġthen Ġaggreg ates Ġthe Ġcounts . ĠTo Ġthis Ġend , Ġwe Ġuse Ġthe Ġsame Ġfeature Ġrepresentation Ġsteps Ġand Ġdata Ġwith Ġthe Ġones Ġused Ġfor Ġfine Ġgr ained Ġclassification Ġ( Section Ġ3 . 2 ). ĠNote Ġthat Ġthe Ġdata Ġof Ġthe Ġtask Ġare Ġassociated Ġwith Ġsubjects Ġ( described Ġin Ġfull Ġdetail Ġat ĠB IB REF 13 Ġ), Ġand , Ġhence , Ġquant ification Ġis Ġperformed Ġfor Ġthe Ġtweets Ġof Ġa Ġsubject . ĠFor Ġeach Ġof Ġthe Ġfive Ġcategories , Ġthe Ġoutput Ġof Ġthe Ġapproach Ġis Ġa Ġ5 - dimensional Ġvector Ġwith Ġthe Ġestimated Ġprevalence Ġof Ġthe Ġcategories . </s> The Ġevaluation Ġmeasure Ġfor Ġthe Ġproblem Ġis Ġthe ĠEarth ĠMo vers ĠDistance Ġ( EM D ) ĠB IB REF 18 Ġ. ĠE MD Ġis Ġa Ġmeasure Ġof Ġerror , Ġhence Ġlower Ġvalues Ġare Ġbetter . ĠIt Ġassumes Ġordered Ġcategories , Ġwhich Ġin Ġour Ġproblem Ġis Ġnaturally Ġdefined . ĠFurther Ġassuming Ġthat Ġthe Ġdistance Ġof Ġconsecutive Ġcategories Ġ( e . g ., ĠPositive Ġand ĠVery Pos itive ) Ġis Ġ1 , Ġthe Ġmeasure Ġis Ġcalculated Ġby : ĠIN LINE FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġnumber Ġof Ġcategories Ġ( five Ġin Ġour Ġcase ) Ġand ĠIN LINE FORM 1 Ġand ĠIN LINE FORM 2 Ġare Ġthe Ġtrue Ġand Ġpredicted Ġprevalence Ġrespectively ĠB IB REF 19 Ġ. </s> Results ĠTable ĠT AB REF 13 Ġpresents Ġthe Ġresults Ġof Ġaugment ing Ġthe Ġfeature Ġset Ġwith Ġthe Ġproposed Ġfeatures . ĠWe Ġuse ĠLog istic ĠReg ression Ġas Ġa Ġbase Ġclass ifier Ġfor Ġthe Ġclassify Ġand Ġcount Ġapproach . ĠNotice Ġthe Ġpositive Ġimpact Ġof Ġthe Ġfeatures Ġin Ġthe Ġperformance Ġin Ġthe Ġtask . ĠAdding Ġthe Ġfeatures Ġderived Ġfrom Ġclust ering Ġthe Ġembed d ings Ġconsistently Ġimproves Ġthe Ġperformance . ĠInterestingly , Ġthe Ġbest Ġperformance Ġ( ĠIN LINE FORM 0 Ġ) Ġis Ġachieved Ġusing Ġthe Ġout - of - domain Ġvectors , Ġas Ġin Ġthe ĠN ER Ġclassification Ġtask . ĠAlso , Ġnotice Ġhow Ġthe Ġapproach Ġimproves Ġover Ġthe Ġstate - of - the - art Ġperformance Ġin Ġthe Ġchallenge Ġ( ĠIN LINE FORM 1 Ġ) ĠB IB REF 13 Ġ, Ġheld Ġby Ġthe Ġmethod Ġof ĠB IB REF 20 Ġ. ĠThe Ġimprovement Ġover Ġthe Ġmethod Ġof ĠB IB REF 20 Ġhowever , Ġdoes Ġnot Ġnecessarily Ġmean Ġthat Ġclassify Ġand Ġcount Ġperforms Ġbetter Ġin Ġthe Ġtask . ĠIt Ġimplies Ġthat Ġthe Ġfeature Ġset Ġwe Ġused Ġis Ġricher , Ġthat Ġin Ġturn Ġhighlights Ġthe Ġvalue Ġof Ġrobust Ġfeature Ġextraction Ġmechanisms Ġwhich Ġis Ġthe Ġsubject Ġof Ġthis Ġpaper . </s> Conclusion </s> We Ġhave Ġshown Ġempir ically Ġthe Ġeffectiveness Ġof Ġincorporating Ġcluster Ġmembership Ġfeatures Ġin Ġthe Ġfeature Ġextraction Ġpipeline Ġof ĠNamed - Entity Ġrecognition , Ġsentiment Ġclassification Ġand Ġquant ification Ġtasks . ĠOur Ġresults Ġstrongly Ġsuggest Ġthat Ġincorporating Ġcluster Ġmembership Ġfeatures Ġbenefit Ġthe Ġperformance Ġin Ġthe Ġtasks . ĠThe Ġfact Ġthat Ġthe Ġperformance Ġimprovements Ġare Ġconsistent Ġin Ġthe Ġfour Ġtasks Ġwe Ġinvestigated , Ġfurther Ġhighlights Ġtheir Ġusefulness , Ġboth Ġfor Ġpractitioners Ġand Ġresearchers . </s> Although Ġour Ġstudy Ġdoes Ġnot Ġidentify Ġa Ġclear Ġwinner Ġwith Ġrespect Ġto Ġthe Ġtype Ġof Ġword Ġvectors Ġ( skip gram , Ġc bow , Ġor ĠGl o Ve ), Ġour Ġfindings Ġsuggest Ġthat Ġone Ġshould Ġfirst Ġtry Ġskip - gram Ġembed d ings Ġof Ġlow Ġdimension ality Ġ( ĠIN LINE FORM 0 Ġ) Ġand Ġhigh Ġnumber Ġof Ġclusters Ġ( e . g ., ĠIN LINE FORM 1 Ġ) Ġas Ġthe Ġresults Ġobtained Ġusing Ġthese Ġsettings Ġare Ġconsistently Ġcompetitive . ĠOur Ġresults Ġalso Ġsuggest Ġthat Ġusing Ġout - of - domain Ġdata , Ġlike ĠWikipedia Ġarticles Ġin Ġthis Ġcase , Ġto Ġconstruct Ġthe Ġword Ġembed d ings Ġis Ġa Ġgood Ġpractice , Ġas Ġthe Ġresults Ġwe Ġobtained Ġwith Ġthese Ġvectors Ġare Ġalso Ġcompetitive . ĠThe Ġpositive Ġof Ġout - of - domain Ġembed d ings Ġand Ġtheir Ġcombination Ġwith Ġin - domain Ġones Ġremains Ġto Ġbe Ġfurther Ġstudied .