7.36% on accuracy and 9.69% on F1 score
EXTRACTIVE
it has 0.024 improvement in accuracy comparing to ELMO Only and  0.006 improvement in F1 score  comparing to ELMO Only too
ABSTRACTIVE
The last group contains the results of our proposed model with different settings. We used Topic Only setting on top of the base model, where we only added the topic-attention layer, and all the word embeddings were from our pre-trained Doc2vec model and were fine-tuned during back propagation. We could observe that compared with the base model, we have an improvement of 7.36% on accuracy and 9.69% on F1 score. Another model (ELMo Only) is to initialize the word embeddings of the base model using the pre-trained ELMo model, and here no topic information was added. Here we have higher scores than Topic Only, and the accuracy and F1 score increased by 9.87% and 12.26% respectively compared with the base model. We then conducted a combination of both(ELMo+Topic), where the word embeddings from the sentences were computed from the pre-trained ELMo model, and the topic representations were from the pre-trained Doc2vec model. We have a remarkable increase of 12.27% on the accuracy and 14.86% on F1 score.
The last group contains the results of our proposed model with different settings. We used Topic Only setting on top of the base model, where we only added the topic-attention layer, and all the word embeddings were from our pre-trained Doc2vec model and were fine-tuned during back propagation. We could observe that compared with the base model, we have an improvement of 7.36% on accuracy and 9.69% on F1 score. Another model (ELMo Only) is to initialize the word embeddings of the base model using the pre-trained ELMo model, and here no topic information was added. Here we have higher scores than Topic Only, and the accuracy and F1 score increased by 9.87% and 12.26% respectively compared with the base model. We then conducted a combination of both(ELMo+Topic), where the word embeddings from the sentences were computed from the pre-trained ELMo model, and the topic representations were from the pre-trained Doc2vec model. We have a remarkable increase of 12.27% on the accuracy and 14.86% on F1 score. FLOAT SELECTED: Figure 1: Topic-attention Model
1910.14076
Introduction </s> Medical Ġtext Ġmining Ġis Ġan Ġexciting Ġarea Ġand Ġis Ġbecoming Ġattractive Ġto Ġnatural Ġlanguage Ġprocessing Ġ( N LP ) Ġresearchers . ĠClinical Ġnotes Ġare Ġan Ġexample Ġof Ġtext Ġin Ġthe Ġmedical Ġarea Ġthat Ġrecent Ġwork Ġhas Ġfocused Ġon ĠB IB REF 0 , ĠB IB REF 1 , ĠB IB REF 2 . ĠThis Ġwork Ġstudies Ġabbre viation Ġdis amb ig uation Ġon Ġclinical Ġnotes ĠB IB REF 3 , ĠB IB REF 4 , Ġspecifically Ġthose Ġused Ġcommonly Ġby Ġphysicians Ġand Ġnurses . ĠSuch Ġclinical Ġabbrevi ations Ġcan Ġhave Ġa Ġlarge Ġnumber Ġof Ġmeanings , Ġdepending Ġon Ġthe Ġspecialty ĠB IB REF 5 , ĠB IB REF 6 . ĠFor Ġexample , Ġthe Ġterm ĠMR Ġcan Ġmean Ġmagnetic Ġresonance , Ġmit ral Ġreg urg itation , Ġmental Ġretard ation , Ġmedical Ġrecord Ġand Ġthe Ġgeneral ĠEnglish ĠMister Ġ( Mr .). ĠTable ĠT AB REF 1 Ġillustrates Ġsuch Ġan Ġexample . ĠAb bre viation Ġdis amb ig uation Ġis Ġan Ġimportant Ġtask Ġin Ġmedical Ġtext Ġunderstanding Ġtask ĠB IB REF 7 . ĠSuccess ful Ġrecognition Ġof Ġthe Ġabbrevi ations Ġin Ġthe Ġnotes Ġcan Ġcontribute Ġto Ġdownstream Ġtasks Ġsuch Ġas Ġclassification , Ġnamed Ġentity Ġrecognition , Ġand Ġrelation Ġextraction ĠB IB REF 7 . </s> Recent Ġwork Ġfocuses Ġon Ġform ulating Ġthe Ġabbre viation Ġdis amb ig uation Ġtask Ġas Ġa Ġclassification Ġproblem , Ġwhere Ġthe Ġpossible Ġsenses Ġof Ġa Ġgiven Ġabbre viation Ġterm Ġare Ġpre - defined Ġwith Ġthe Ġhelp Ġof Ġdomain Ġexperts ĠB IB REF 6 , ĠB IB REF 5 . ĠTraditional Ġfeatures Ġsuch Ġas Ġpart - of - speech Ġ( POS ) Ġand ĠTerm ĠFrequency - In verse ĠDocument ĠFrequency Ġ( TF - ID F ) Ġare Ġwidely Ġinvestigated Ġfor Ġclinical Ġnotes Ġclassification . ĠClass ifiers Ġlike Ġsupport Ġvector Ġmachines Ġ( S V Ms ) Ġand Ġrandom Ġforests Ġ( RF s ) Ġare Ġused Ġto Ġmake Ġpredictions ĠB IB REF 1 . ĠSuch Ġmethods Ġdepend Ġheavily Ġon Ġfeature Ġengineering . ĠRecently , Ġdeep Ġfeatures Ġhave Ġbeen Ġinvestigated Ġin Ġthe Ġmedical Ġdomain . ĠWord Ġembed d ings ĠB IB REF 8 Ġand ĠConv olution al ĠNeural ĠNetworks Ġ( CNN s ) ĠB IB REF 9 , ĠB IB REF 10 Ġprovide Ġvery Ġcompetitive Ġperformance Ġon Ġtext Ġclassification Ġfor Ġclinical Ġnotes Ġand Ġabbre viation Ġdis amb ig uation Ġtask ĠB IB REF 0 , ĠB IB REF 11 , ĠB IB REF 12 , ĠB IB REF 6 . ĠBeyond Ġvanilla Ġembed d ings , ĠB IB REF 13 Ġutilized Ġcontextual Ġfeatures Ġto Ġdo Ġabbre viation Ġexpansion . ĠAnother Ġchallenge Ġis Ġthe Ġdifficulty Ġin Ġobtaining Ġtraining Ġdata : Ġclinical Ġnotes Ġhave Ġmany Ġrestrictions Ġdue Ġto Ġprivacy Ġissues Ġand Ġrequire Ġdomain Ġexperts Ġto Ġdevelop Ġhigh - quality Ġannotations , Ġthus Ġleading Ġto Ġlimited Ġannot ated Ġtraining Ġand Ġtesting Ġdata . ĠAnother Ġdifficulty Ġis Ġthat Ġin Ġthe Ġreal Ġworld Ġ( and Ġin Ġthe Ġexisting Ġpublic Ġdatasets ), Ġsome Ġabbre viation Ġterm - sense Ġpairs Ġ( for Ġexample , ĠAB Ġas Ġabortion ) Ġhave Ġa Ġvery Ġhigh Ġfrequency Ġof Ġoccurrence ĠB IB REF 5 , Ġwhile Ġothers Ġare Ġrarely Ġfound . ĠThis Ġlong Ġtail Ġissue Ġcreates Ġthe Ġchallenge Ġof Ġtraining Ġunder Ġun balanced Ġsample Ġdistributions . ĠWe Ġtackle Ġthese Ġproblems Ġin Ġthe Ġsetting Ġof Ġfew - shot Ġlearning ĠB IB REF 14 , ĠB IB REF 15 Ġwhere Ġonly Ġa Ġfew Ġor Ġa Ġlow Ġnumber Ġof Ġsamples Ġcan Ġbe Ġfound Ġin Ġthe Ġtraining Ġdataset Ġto Ġmake Ġuse Ġof Ġlimited Ġresources . ĠWe Ġpropose Ġa Ġmodel Ġthat Ġcombines Ġdeep Ġcontextual Ġfeatures Ġbased Ġon ĠEL Mo ĠB IB REF 16 Ġand Ġtopic Ġinformation Ġto Ġsolve Ġthe Ġabbre viation Ġdis amb ig uation Ġtask . </s> Our Ġcontributions Ġcan Ġbe Ġsummarized Ġas : Ġ1 ) Ġwe Ġre - ex am ined Ġand Ġcorrected Ġan Ġexisting Ġdataset Ġfor Ġtraining Ġand Ġwe Ġcollected Ġa Ġtest Ġset Ġfor Ġevaluation Ġwith Ġfocus Ġespecially Ġfor Ġrare Ġsenses ; Ġ2 ) Ġwe Ġproposed Ġa Ġfew - shot Ġlearning Ġapproach Ġwhich Ġcombines Ġtopic Ġinformation Ġand Ġcontextual ized Ġword Ġembed d ings Ġto Ġsolve Ġclinical Ġabbre viation Ġdis amb ig uation Ġtask . ĠThe Ġimplementation Ġis Ġavailable Ġonline ; Ġ3 ) Ġas Ġlimited Ġresearch Ġare Ġconducted Ġon Ġthis Ġparticular Ġtask , Ġwe Ġevaluated Ġand Ġcompared Ġa Ġnumber Ġof Ġbaseline Ġmethods Ġincluding Ġclassical Ġmodels Ġand Ġdeep Ġmodels Ġcomprehens ively . </s> Dat as ets </s> Training ĠDat as et ĠUM ĠInventory ĠB IB REF 5 Ġis Ġa Ġpublic Ġdataset Ġcreated Ġby Ġresearchers Ġfrom Ġthe ĠUniversity Ġof ĠMinnesota , Ġcontaining Ġabout Ġ37 , 500 Ġtraining Ġsamples Ġwith Ġ75 Ġabbre viation Ġterms . ĠEx isting Ġwork Ġreports Ġabbre viation Ġdis amb ig uation Ġresults Ġon Ġ50 Ġabbre viation Ġterms ĠB IB REF 6 , ĠB IB REF 5 , ĠB IB REF 17 . ĠHowever , Ġafter Ġcarefully Ġreviewing Ġthis Ġdataset , Ġwe Ġfound Ġthat Ġit Ġcontains Ġmany Ġsamples Ġwhere Ġmedical Ġprofessionals Ġdisagree : Ġwrong Ġsamples Ġand Ġunc ategor ized Ġsamples . ĠDue Ġto Ġthese Ġmistakes Ġand Ġflaws Ġof Ġthis Ġdataset , Ġwe Ġremoved Ġthe Ġerroneous Ġsamples Ġand Ġeventually Ġselected Ġ30 Ġabbre viation Ġterms Ġas Ġour Ġtraining Ġdataset Ġthat Ġcan Ġbe Ġmade Ġpublic . ĠAmong Ġall Ġthe Ġabbre viation Ġterms , Ġwe Ġhave Ġ11 , 466 Ġsamples , Ġand Ġ93 Ġterm - sense Ġpairs Ġin Ġtotal Ġ( on Ġaverage Ġ123 . 3 Ġsamples / term - sense Ġpair Ġand Ġ38 2 . 2 Ġsamples / term ). ĠSome Ġterm - sense Ġpairs Ġare Ġvery Ġpopular Ġwith Ġa Ġlarger Ġnumber Ġof Ġtraining Ġsamples Ġbut Ġsome Ġare Ġnot Ġ( typically Ġless Ġthan Ġ5 ), Ġwe Ġcall Ġthem Ġrare - sense Ġcases Ġ. ĠMore Ġdetails Ġcan Ġbe Ġfound Ġin ĠAppendix ĠSEC REF 7 . </s> Testing ĠDat as et ĠOur Ġtesting Ġdataset Ġtakes ĠM IM IC - III ĠB IB REF 18 Ġand ĠPubMed Ġas Ġdata Ġsources . ĠHere Ġwe Ġare Ġreferring Ġto Ġall Ġthe Ġnotes Ġdata Ġfrom ĠM IM IC - III Ġ( NOT EE V ENTS Ġtable Ġ) Ġand ĠCase ĠReports Ġarticles Ġfrom ĠPubMed , Ġas Ġthis Ġtype Ġof Ġcontents Ġare Ġclose Ġto Ġmedical Ġnotes . ĠWe Ġprovide Ġdetailed Ġinformation Ġin ĠAppendix ĠSEC REF 8 , Ġincluding Ġthe Ġsteps Ġto Ġcreate Ġthe Ġtesting Ġdataset . ĠEventually , Ġwe Ġhave Ġa Ġbalanced Ġtesting Ġdataset , Ġwhere Ġeach Ġterm - sense Ġpair Ġhas Ġat Ġleast Ġ11 Ġand Ġup Ġto Ġ15 Ġsamples Ġfor Ġtraining Ġ( on Ġaverage , Ġeach Ġpair Ġhas Ġ14 . 56 Ġsamples Ġand Ġthe Ġmedian Ġsample Ġnumber Ġis Ġ15 ). </s> Bas elines </s> We Ġconducted Ġa Ġcomprehensive Ġcomparison Ġwith Ġthe Ġbaseline Ġmodels , Ġand Ġsome Ġof Ġthem Ġwere Ġnever Ġinvestigated Ġfor Ġthe Ġabbre viation Ġdis amb ig uation Ġtask . ĠWe Ġapplied Ġtraditional Ġfeatures Ġby Ġsimply Ġtaking Ġthe ĠTF - ID F Ġfeatures Ġas Ġthe Ġinputs Ġinto Ġthe Ġclassic Ġclass ifiers . ĠDeep Ġfeatures Ġare Ġalso Ġconsidered : Ġa ĠDoc 2 vec Ġmodel ĠB IB REF 19 Ġwas Ġpre - trained Ġusing ĠG ens im Ġand Ġthese Ġword Ġembed d ings Ġwere Ġapplied Ġto Ġinitialize Ġdeep Ġmodels Ġand Ġfine - tun ed . </s> TF - ID F : ĠWe Ġapplied ĠTF - ID F Ġfeatures Ġas Ġinputs Ġto Ġfour Ġclass ifiers : Ġsupport Ġvector Ġmachine Ġclass ifier Ġ( S VM ), Ġlog istic Ġregression Ġclass ifier Ġ( LR ), ĠNa ive ĠBay es Ġclass ifier Ġ( NB ) Ġand Ġrandom Ġforest Ġ( RF ); ĠCNN : ĠWe Ġfollowed Ġthe Ġsame Ġarchitecture Ġas ĠB IB REF 9 ; ĠL ST M : ĠWe Ġapplied Ġan ĠL ST M Ġmodel ĠB IB REF 20 Ġto Ġclassify Ġthe Ġsentences Ġwith Ġpre - trained Ġword Ġembed d ings ; L ST M - soft : ĠWe Ġthen Ġadded Ġa Ġsoft - att ention ĠB IB REF 21 Ġlayer Ġon Ġtop Ġof Ġthe ĠL ST M Ġmodel Ġwhere Ġwe Ġcomputed Ġsoft Ġalignment Ġscores Ġover Ġeach Ġof Ġthe Ġhidden Ġstates ; ĠL ST M - self : ĠWe Ġapplied Ġa Ġself - att ention Ġlayer ĠB IB REF 22 Ġto ĠL ST M Ġmodel . ĠWe Ġdenote Ġthe Ġcontent Ġvector Ġas Ġ$ c _ i $ Ġfor Ġeach Ġsentence Ġ$ i $, Ġas Ġthe Ġinput Ġto Ġthe Ġlast Ġclassification Ġlayer . </s> Topic - att ention ĠModel </s> EL Mo ĠEL Mo Ġis Ġa Ġnew Ġtype Ġof Ġword Ġrepresentation Ġintroduced Ġby ĠB IB REF 16 Ġwhich Ġconsiders Ġcontextual Ġinformation Ġcaptured Ġby Ġpre - trained ĠBi L ST M Ġlanguage Ġmodels . ĠSimilar Ġworks Ġlike ĠB ERT ĠB IB REF 23 Ġand ĠBio BER T ĠB IB REF 24 Ġalso Ġconsider Ġcontext Ġbut Ġwith Ġdeeper Ġstructures . ĠCompared Ġwith Ġthose Ġmodels , ĠEL Mo Ġhas Ġless Ġparameters Ġand Ġnot Ġeasy Ġto Ġbe Ġover fitting . ĠWe Ġtrained Ġour ĠEL Mo Ġmodel Ġon Ġthe ĠM IM IC - III Ġcorpus . ĠSince Ġsome Ġsentences Ġalso Ġappear Ġin Ġthe Ġtest Ġset , Ġone Ġmay Ġraise Ġthe Ġconcern Ġof Ġperformance Ġinflation Ġin Ġtesting . ĠHowever , Ġwe Ġpre - trained ĠEL Mo Ġusing Ġthe Ġwhole Ġcorpus Ġof ĠM IM IC Ġin Ġan Ġun super vised Ġway , Ġso Ġthe Ġclassification Ġlabels Ġwere Ġnot Ġvisible Ġduring Ġtraining . ĠTo Ġtrain Ġthe ĠEL Mo Ġmodel , Ġwe Ġadapted Ġcode Ġfrom ĠAllen N LP , Ġwe Ġset Ġthe Ġword Ġembed ding Ġdimension Ġto Ġ64 Ġand Ġapplied Ġtwo ĠBi L ST M Ġlayers . ĠFor Ġall Ġof Ġour Ġexperiments Ġthat Ġinvolved Ġwith ĠEL Mo , Ġwe Ġinitialized Ġthe Ġword Ġembed d ings Ġfrom Ġthe Ġlast Ġlayer Ġof ĠEL Mo . </s> Topic - att ention ĠWe Ġpropose Ġa Ġneural Ġtopic - att ention Ġmodel Ġfor Ġtext Ġclassification . ĠOur Ġassumption Ġis Ġthat Ġthe Ġtopic Ġinformation Ġplays Ġan Ġimportant Ġrole Ġin Ġdoing Ġthe Ġdis amb ig uation Ġgiven Ġa Ġsentence . ĠFor Ġexample , Ġthe Ġabbre viation Ġof Ġthe Ġmedical Ġterm ĠF ISH Ġhas Ġtwo Ġpotential Ġsenses : Ġgeneral ĠEnglish Ġfish Ġas Ġseafood Ġand Ġthe Ġsense Ġof Ġfluorescent Ġin Ġsitu Ġhybrid ization ĠB IB REF 17 . ĠThe Ġformer Ġcase Ġalways Ġgoes Ġwith Ġthe Ġtopic Ġof Ġfood , Ġallergies Ġwhile Ġthe Ġother Ġone Ġappears Ġin Ġthe Ġtopic Ġof Ġsome Ġexamination Ġtest Ġreports . ĠIn Ġour Ġmodel , Ġa Ġtopic - att ention Ġmodule Ġis Ġapplied Ġto Ġadd Ġtopic Ġinformation Ġinto Ġthe Ġfinal Ġsentence Ġrepresentation . ĠThis Ġmodule Ġcalculates Ġthe Ġdistribution Ġof Ġtopic - att ention Ġweights Ġon Ġa Ġlist Ġof Ġtopic Ġvectors . ĠAs Ġshown Ġin ĠFigure ĠSEC REF 5 , Ġwe Ġtook Ġthe Ġcontent Ġvector Ġ$ c _ i $ Ġ( from Ġsoft - att ention ĠB IB REF 22 ) Ġand Ġa Ġtopic Ġmatrix Ġ$ T _{ topic } =[ t _ 1 , t _ 2 , .. , t _ j ] $ Ġ( where Ġeach Ġ$ t _ i $ Ġis Ġa Ġcolumn Ġvector Ġin Ġthe Ġfigure Ġand Ġwe Ġillustrate Ġfour Ġtopics ) Ġas Ġthe Ġinputs Ġto Ġthe Ġtopic - att ention Ġmodule , Ġand Ġthen Ġcalculated Ġa Ġweighted Ġsumm ation Ġof Ġthe Ġtopic Ġvectors . ĠThe Ġfinal Ġsentence Ġrepresentation Ġ$ r _ i $ Ġfor Ġthe Ġsentence Ġ$ i $ Ġwas Ġcalculated Ġas Ġthe Ġfollowing : </s> where Ġ$ W _{ topic } $ Ġand Ġ$ b Ġ_ Ġ{ Ġtopic Ġ} $ Ġare Ġtrain able Ġparameters , Ġ$\ beta Ġ_ { it } $ Ġrepresents Ġthe Ġtopic - att ention Ġweights . ĠThe Ġfinal Ġsentence Ġrepresentation Ġis Ġden oted Ġas Ġ$ r _ i $, Ġand Ġ$ [ \ cd ot Ġ, \ cd ot Ġ] $ Ġmeans Ġconc aten ation . ĠHere Ġ$ s _ i $ Ġis Ġthe Ġrepresentation Ġof Ġthe Ġsentence Ġwhich Ġincludes Ġthe Ġtopic Ġinformation . ĠThe Ġfinal Ġsentence Ġrepresentation Ġ$ r _ i $ Ġis Ġthe Ġconc aten ation Ġof Ġ$ c _ i $ Ġand Ġ$ s _ i $, Ġnow Ġwe Ġhave Ġboth Ġcontext - aware Ġsentence Ġrepresentation Ġand Ġtopic - related Ġrepresentation . ĠThen Ġwe Ġadded Ġa Ġfully - connected Ġlayer , Ġfollowed Ġby Ġa ĠSoft max Ġto Ġdo Ġclassification Ġwith Ġcross - ent ropy Ġloss Ġfunction . </s> Topic ĠMatrix ĠTo Ġgenerate Ġthe Ġtopic Ġmatrix Ġ$ T _{ topic } $ Ġas Ġin ĠEqu ation ĠDIS PLAY _ FORM 9 , Ġwe Ġpropose Ġa Ġconv olution - based Ġmethod Ġto Ġgenerate Ġtopic Ġvectors . ĠWe Ġfirst Ġpre - trained Ġa Ġtopic Ġmodel Ġusing Ġthe ĠLat ent ĠDir ich let ĠAll ocation Ġ( L DA ) ĠB IB REF 25 Ġmodel Ġon ĠM IM IC - III Ġnotes Ġas Ġused Ġby Ġthe ĠEL Mo Ġmodel . ĠWe Ġset Ġthe Ġnumber Ġof Ġtopics Ġto Ġbe Ġ50 Ġand Ġran Ġfor Ġ30 Ġiterations . ĠThen Ġwe Ġwere Ġable Ġto Ġget Ġa Ġlist Ġof Ġtop Ġ$ k $ Ġwords Ġ( we Ġset Ġ$ k = 100 $ ) Ġfor Ġeach Ġtopic . ĠTo Ġget Ġthe Ġtopic Ġvector Ġ$ t $ Ġfor Ġa Ġspecific Ġtopic : </s> where Ġ$ e _ j $ Ġ( column Ġvector ) Ġis Ġthe Ġpre - trained ĠDoc 2 vec Ġword Ġembed ding Ġfor Ġthe Ġtop Ġword Ġ$ j $ Ġfrom Ġthe Ġcurrent Ġtopic , Ġand Ġ$ Con v (\ cd ot Ġ) $ Ġindicates Ġa Ġconv olution al Ġlayer ; Ġ$ max pool (\ cd ot Ġ) $ Ġis Ġa Ġmax Ġpool ing Ġlayer . ĠWe Ġfinally Ġresh aped Ġthe Ġoutput Ġ$ t $ Ġinto Ġa Ġ1 - dimension Ġvector . ĠEventually Ġwe Ġcollected Ġall Ġtopic Ġvectors Ġas Ġthe Ġtopic Ġmatrix Ġ$ T _{ topic } $ Ġin ĠFigure ĠSEC REF 5 . </s> E val uation </s> We Ġdid Ġthree Ġgroups Ġof Ġexperiments Ġincluding Ġtwo Ġbaseline Ġgroups Ġand Ġour Ġproposed Ġmodel . ĠThe Ġfirst Ġgroup Ġwas Ġthe ĠTF - ID F Ġfeatures Ġin ĠSection ĠSEC REF 3 Ġfor Ġtraditional Ġmodels . ĠThe ĠNa Ã¯ve ĠBay esian Ġclass ifier Ġ( NB ) Ġhas Ġthe Ġhighest Ġscores Ġamong Ġall Ġtraditional Ġmethods . ĠThe Ġsecond Ġgroup Ġof Ġexperiments Ġused Ġneural Ġmodels Ġdescribed Ġin ĠSection ĠSEC REF 3 , Ġwhere ĠL ST M Ġwith Ġself Ġattention Ġmodel Ġ( L ST M - self ) Ġhas Ġcompetitive Ġresults Ġamong Ġthis Ġgroup , Ġwe Ġchoose Ġthis Ġmodel Ġas Ġour Ġbase Ġmodel . ĠNotably , Ġthis Ġis Ġthe Ġfirst Ġstudy Ġthat Ġcompares Ġand Ġevaluates ĠL ST M - based Ġmodels Ġon Ġthe Ġmedical Ġterm Ġabbre viation Ġdis amb ig uation Ġtask . </s> table Exper imental ĠResults : Ġwe Ġreport Ġmacro ĠF 1 Ġin Ġall Ġthe Ġexperiments . Ġfigure Topic - att ention ĠModel </s> The Ġlast Ġgroup Ġcontains Ġthe Ġresults Ġof Ġour Ġproposed Ġmodel Ġwith Ġdifferent Ġsettings . ĠWe Ġused ĠTopic ĠOnly Ġsetting Ġon Ġtop Ġof Ġthe Ġbase Ġmodel , Ġwhere Ġwe Ġonly Ġadded Ġthe Ġtopic - att ention Ġlayer , Ġand Ġall Ġthe Ġword Ġembed d ings Ġwere Ġfrom Ġour Ġpre - trained ĠDoc 2 vec Ġmodel Ġand Ġwere Ġfine - tun ed Ġduring Ġback Ġpropagation . ĠWe Ġcould Ġobserve Ġthat Ġcompared Ġwith Ġthe Ġbase Ġmodel , Ġwe Ġhave Ġan Ġimprovement Ġof Ġ7 . 36 % Ġon Ġaccuracy Ġand Ġ9 . 69 % Ġon ĠF 1 Ġscore . ĠAnother Ġmodel Ġ( EL Mo ĠOnly ) Ġis Ġto Ġinitialize Ġthe Ġword Ġembed d ings Ġof Ġthe Ġbase Ġmodel Ġusing Ġthe Ġpre - trained ĠEL Mo Ġmodel , Ġand Ġhere Ġno Ġtopic Ġinformation Ġwas Ġadded . ĠHere Ġwe Ġhave Ġhigher Ġscores Ġthan ĠTopic ĠOnly , Ġand Ġthe Ġaccuracy Ġand ĠF 1 Ġscore Ġincreased Ġby Ġ9 . 87 % Ġand Ġ12 . 26 % Ġrespectively Ġcompared Ġwith Ġthe Ġbase Ġmodel . ĠWe Ġthen Ġconducted Ġa Ġcombination Ġof Ġboth ( EL Mo + Topic ), Ġwhere Ġthe Ġword Ġembed d ings Ġfrom Ġthe Ġsentences Ġwere Ġcomputed Ġfrom Ġthe Ġpre - trained ĠEL Mo Ġmodel , Ġand Ġthe Ġtopic Ġrepresentations Ġwere Ġfrom Ġthe Ġpre - trained ĠDoc 2 vec Ġmodel . ĠWe Ġhave Ġa Ġremarkable Ġincrease Ġof Ġ12 . 27 % Ġon Ġthe Ġaccuracy Ġand Ġ14 . 86 % Ġon ĠF 1 Ġscore . </s> To Ġfurther Ġcompare Ġour Ġproposed Ġtopic - att ention Ġmodel Ġand Ġthe Ġbase Ġmodel , Ġwe Ġreport Ġan Ġaverage Ġof Ġarea Ġunder Ġthe Ġcurve ( A UC ) Ġscore Ġamong Ġall Ġ30 Ġterms : Ġthe Ġbase Ġmodel Ġhas Ġan Ġaverage ĠA UC Ġof Ġ0 . 7 189 , Ġand Ġour Ġtopic - att ention Ġmodel Ġ( EL Mo + Topic ) Ġachieves Ġan Ġaverage ĠA UC Ġof Ġ0 . 8 196 . ĠWe Ġprovide Ġa Ġcase Ġstudy Ġin ĠAppendix ĠSEC REF 9 . ĠThe Ġresults Ġshow Ġthat Ġthe Ġmodel Ġcan Ġbenefit Ġfrom ĠEL Mo , Ġwhich Ġconsiders Ġcontextual Ġfeatures , Ġand Ġthe Ġtopic - att ention Ġmodule , Ġwhich Ġbrings Ġin Ġtopic Ġinformation . ĠWe Ġcan Ġconclude Ġthat Ġunder Ġthe Ġfew - shot Ġlearning Ġsetting , Ġour Ġproposed Ġmodel Ġcan Ġbetter Ġcapture Ġthe Ġsentence Ġfeatures Ġwhen Ġonly Ġlimited Ġtraining Ġsamples Ġare Ġexplored Ġin Ġa Ġsmall - scale Ġdataset . </s> Conclusion </s> In Ġthis Ġpaper , Ġwe Ġpropose Ġa Ġneural Ġtopic - att ention Ġmodel Ġwith Ġfew - shot Ġlearning Ġfor Ġmedical Ġabbre viation Ġdis amb ig uation Ġtask . ĠWe Ġalso Ġmanually Ġcleaned Ġand Ġcollected Ġtraining Ġand Ġtesting Ġdata Ġfor Ġthis Ġtask , Ġwhich Ġwe Ġrelease Ġto Ġpromote Ġrelated Ġresearch Ġin ĠN LP Ġwith Ġclinical Ġnotes . ĠIn Ġaddition , Ġwe Ġevaluated Ġand Ġcompared Ġa Ġcomprehensive Ġset Ġof Ġbaseline Ġmodels , Ġsome Ġof Ġwhich Ġhad Ġnever Ġbeen Ġapplied Ġto Ġthe Ġmedical Ġterm Ġabbre viation Ġdis amb ig uation Ġtask . ĠFuture Ġwork Ġwould Ġbe Ġto Ġadapt Ġother Ġmodels Ġlike ĠBio BER T Ġor ĠB ERT Ġto Ġour Ġproposed Ġtopic - att ention Ġmodel . ĠWe Ġwill Ġalso Ġextend Ġthe Ġmethod Ġinto Ġother Ġclinical Ġnotes Ġtasks Ġsuch Ġas Ġdrug Ġname Ġrecognition Ġand ĠI CD - 9 Ġcode Ġauto - ass igning ĠB IB REF 26 . ĠIn Ġaddition , Ġother ĠL DA - based Ġapproach Ġcan Ġbe Ġinvestigated . </s> Dat as et ĠDetails </s> Figure ĠFIG REF 11 Ġshows Ġthe Ġhist ogram Ġfor Ġthe Ġdistribution Ġof Ġterm - sense Ġpair Ġsample Ġnumbers . ĠThe ĠX - axis Ġgives Ġthe Ġpair Ġsample Ġnumbers Ġand Ġthe ĠY - axis Ġshows Ġthe Ġcounts . ĠFor Ġexample , Ġthe Ġfirst Ġbar Ġshows Ġthat Ġthere Ġare Ġ43 Ġterm - sense Ġpairs Ġthat Ġhave Ġthe Ġsample Ġnumber Ġin Ġthe Ġrange Ġof Ġ0 - 50 . </s> We Ġalso Ġshow Ġhist ogram Ġof Ġclass Ġnumbers Ġfor Ġall Ġterms Ġin ĠFigure ĠFIG REF 11 . ĠThe ĠY - axis Ġgives Ġthe Ġcounts Ġwhile Ġthe ĠX - axis Ġgives Ġthe Ġnumber Ġof Ġclasses . ĠFor Ġinstance , Ġthe Ġfirst Ġbar Ġmeans Ġthere Ġare Ġ12 Ġterms Ġcontain Ġ2 Ġclasses . </s> Testing ĠDat as et </s> Since Ġthe Ġtraining Ġdataset Ġis Ġun balanced Ġand Ġrelatively Ġsmall , Ġit Ġis Ġhard Ġto Ġsplit Ġit Ġfurther Ġinto Ġtraining Ġand Ġtesting . ĠEx isting Ġwork ĠB IB REF 6 , ĠB IB REF 5 Ġconducted Ġfold Ġvalidation Ġon Ġthe Ġdatasets Ġand Ġwe Ġfound Ġthere Ġare Ġextreme Ġrare Ġsenses Ġfor Ġwhich Ġonly Ġone Ġor Ġtwo Ġsamples Ġexist . ĠBesides , Ġwe Ġbelieve Ġthat Ġit Ġis Ġbetter Ġto Ġevaluate Ġon Ġa Ġbalanced Ġdataset Ġto Ġdetermine Ġwhether Ġit Ġperforms Ġequally Ġwell Ġon Ġall Ġclasses . ĠWhile Ġmost Ġof Ġthe Ġworks Ġdeal Ġwith Ġun balanced Ġtraining Ġand Ġtesting Ġthat Ġwhich Ġmay Ġlead Ġto Ġvery Ġhigh Ġaccuracy Ġif Ġthere Ġis Ġa Ġdominating Ġclass Ġin Ġboth Ġtraining Ġand Ġtesting Ġset , Ġthe Ġmodel Ġmay Ġhave Ġa Ġpoor Ġperformance Ġin Ġrare Ġtesting Ġcases Ġbecause Ġonly Ġa Ġfew Ġsamples Ġhave Ġbeen Ġseen Ġduring Ġtraining . ĠTo Ġbe Ġfair Ġto Ġall Ġthe Ġclasses , Ġa Ġgood Ġperformance Ġon Ġthese Ġrare Ġcases Ġis Ġalso Ġrequired Ġotherwise Ġit Ġmay Ġlead Ġto Ġa Ġsevere Ġsituation . ĠIn Ġthis Ġwork , Ġwe Ġare Ġvery Ġinterested Ġto Ġsee Ġhow Ġthe Ġmodel Ġworks Ġamong Ġall Ġsenses Ġespecially Ġthe Ġrare Ġones . ĠAlso , Ġwe Ġcan Ġprevent Ġthe Ġmodel Ġfrom Ġtriv ially Ġpredicting Ġthe Ġdominant Ġclass Ġand Ġachieving Ġhigh Ġaccuracy . ĠAs Ġa Ġresult , Ġwe Ġdecided Ġto Ġcreate Ġa Ġdataset Ġwith Ġthe Ġsame Ġnumber Ġof Ġsamples Ġfor Ġeach Ġcase Ġin Ġthe Ġtest Ġdataset . </s> Our Ġtesting Ġdataset Ġtakes ĠM IM IC - III ĠB IB REF 18 Ġand ĠPubMed Ġas Ġdata Ġsources . ĠHere Ġwe Ġare Ġreferring Ġto Ġall Ġthe Ġnotes Ġdata Ġfrom ĠM IM IC - III Ġ( NOT EE V ENTS Ġtable Ġ) Ġand ĠCase ĠReports Ġarticles Ġfrom ĠPubMed , Ġas Ġthese Ġcontents Ġare Ġclose Ġto Ġmedical Ġnotes . ĠTo Ġcreate Ġthe Ġtest Ġset , Ġwe Ġfirst Ġfollowed Ġthe Ġapproach Ġby ĠB IB REF 6 Ġwho Ġapplied Ġan Ġauto - gener ating Ġmethod . ĠInitially , Ġwe Ġbuilt Ġa Ġterm - sense Ġdictionary Ġfrom Ġthe Ġtraining Ġdataset . ĠThen Ġwe Ġdid Ġmatching Ġfor Ġthe Ġsense Ġwords Ġor Ġphrases Ġin Ġthe ĠM IM IC - III Ġnotes Ġdataset , Ġand Ġonce Ġthere Ġis Ġa Ġmatch , Ġwe Ġreplaced Ġthe Ġwords Ġor Ġphrases Ġwith Ġthe Ġabbre viation Ġterm Ġ. ĠWe Ġthen Ġasked Ġtwo Ġresearchers Ġwith Ġa Ġmedical Ġbackground Ġto Ġcheck Ġthe Ġmatched Ġsamples Ġmanually Ġwith Ġthe Ġfollowing Ġjudgment : Ġgiven Ġthis Ġsentence Ġand Ġthe Ġabbre viation Ġterm Ġand Ġits Ġsense , Ġdo Ġyou Ġthink Ġthe Ġcontent Ġis Ġenough Ġfor Ġyou Ġto Ġguess Ġthe Ġsense Ġand Ġwhether Ġthis Ġis Ġthe Ġright Ġsense ? ĠTo Ġestimate Ġthe Ġagreement Ġon Ġthe Ġannotations , Ġwe Ġselected Ġa Ġsubset Ġwhich Ġcontains Ġ120 Ġsamples Ġrandomly Ġand Ġlet Ġthe Ġtwo Ġannot ators Ġannot ate Ġindividually . ĠWe Ġgot Ġa ĠKappa Ġscore ĠB IB REF 27 Ġof Ġ0 . 96 , Ġwhich Ġis Ġconsidered Ġas Ġa Ġnear Ġperfect Ġagreement . ĠWe Ġthen Ġdistributed Ġthe Ġwork Ġto Ġthe Ġtwo Ġannot ators , Ġand Ġeach Ġof Ġthem Ġlabeled Ġa Ġhalf Ġof Ġthe Ġdataset , Ġwhich Ġmeans Ġeach Ġsample Ġwas Ġonly Ġlabeled Ġby Ġa Ġsingle Ġannot ator . ĠFor Ġsome Ġrare Ġterm - sense Ġpairs , Ġwe Ġfailed Ġto Ġfind Ġsamples Ġfrom ĠM IM IC - III . ĠThe Ġannot ators Ġthen Ġsearched Ġthese Ġsenses Ġvia ĠPubMed Ġdata Ġsource Ġmanually , Ġaiming Ġto Ġfind Ġclinical Ġnotes - like Ġsentences . ĠThey Ġpicked Ġgood Ġsentences Ġfrom Ġthese Ġresults Ġas Ġtesting Ġsamples Ġwhere Ġthe Ġkeywords Ġexist Ġand Ġthe Ġcontent Ġis Ġinformative . ĠFor Ġthose Ġsenses Ġthat Ġare Ġextremely Ġrare , Ġwe Ġlet Ġthe Ġannot ators Ġcreate Ġsentences Ġin Ġthe Ġclinical Ġnote Ġstyle Ġas Ġtesting Ġsamples Ġaccording Ġto Ġtheir Ġexperiences . ĠEventually , Ġwe Ġhave Ġa Ġbalanced Ġtesting Ġdataset , Ġwhere Ġeach Ġterm - sense Ġpair Ġhas Ġaround Ġ15 Ġsamples Ġfor Ġtesting Ġ( on Ġaverage , Ġeach Ġpair Ġhas Ġ14 . 56 Ġsamples Ġand Ġthe Ġmedian Ġsample Ġnumber Ġis Ġ15 ), Ġand Ġa Ġcomparison Ġwith Ġtraining Ġdataset Ġis Ġshown Ġin ĠFigure ĠFIG REF 11 . ĠDue Ġto Ġthe Ġdifficulty Ġfor Ġcollecting Ġthe Ġtesting Ġdataset , Ġwe Ġdecided Ġto Ġonly Ġcollect Ġfor Ġa Ġrandom Ġselection Ġof Ġ30 Ġterms . ĠOn Ġaverage , Ġit Ġtook Ġfew Ġhours Ġto Ġgenerate Ġtesting Ġsamples Ġfor Ġeach Ġabbre viation Ġterm Ġper Ġresearcher . </s> Case ĠStudy </s> We Ġnow Ġselect Ġtwo Ġrepresentative Ġterms ĠAC , ĠIM Ġand Ġplot Ġtheir Ġreceiver Ġoperating Ġcharacteristic ( R OC ) Ġcurves . ĠThe Ġterm Ġhas Ġa Ġrelatively Ġlarge Ġnumber Ġof Ġclasses Ġand Ġthe Ġsecond Ġone Ġhas Ġextremely Ġun balanced Ġtraining Ġsamples . ĠWe Ġshow Ġthe Ġdetails Ġin ĠTable ĠT AB REF 16 . ĠWe Ġhave Ġ8 Ġclasses Ġin Ġterm ĠAC . ĠFigure ĠFIG REF 15 ( a ) Ġillustrates Ġthe Ġresults Ġof Ġour Ġbest Ġperformed Ġmodel Ġand ĠFigure ĠFIG REF 15 ( b ) Ġshows Ġthe Ġresults Ġof Ġthe Ġbase Ġmodel . ĠThe Ġaccuracy Ġand ĠF 1 Ġscore Ġhave Ġan Ġimprovement Ġfrom Ġ0 . 38 98 Ġand Ġ0 . 28 30 Ġto Ġ0 . 49 15 Ġand Ġ0 . 40 59 Ġrespectively . ĠRegarding Ġthe Ġrare Ġsenses Ġ( for Ġexample , Ġclass Ġ0 , Ġ1 , Ġ4 Ġand Ġ6 ), Ġwe Ġcan Ġobserve Ġan Ġincrease Ġin Ġthe ĠR OC Ġareas . ĠClass Ġ6 Ġhas Ġan Ġobvious Ġimprovement Ġfrom Ġ0 . 75 Ġto Ġ1 . 00 . ĠSuch Ġimprovements Ġin Ġthe Ġrare Ġsenses Ġmake Ġa Ġhuge Ġdifference Ġin Ġthe Ġreported Ġaverage Ġaccuracy Ġand ĠF 1 Ġscore , Ġsince Ġwe Ġhave Ġa Ġnearly Ġequal Ġnumber Ġof Ġsamples Ġfor Ġeach Ġclass Ġin Ġthe Ġtesting Ġdata . ĠSimilarly , Ġwe Ġshow Ġthe Ġplots Ġfor ĠIM Ġterm Ġin ĠFigure ĠFIG REF 15 ( c ) Ġand ĠFIG REF 15 ( d ). ĠIM Ġhas Ġonly Ġtwo Ġclasses , Ġbut Ġthey Ġare Ġvery Ġun balanced Ġin Ġtraining Ġset , Ġas Ġshown Ġin ĠTable ĠT AB REF 16 . ĠThe Ġaccuracy Ġand ĠF 1 Ġscores Ġimproved Ġfrom Ġ0 . 66 67 Ġand Ġ0 . 62 50 Ġto Ġ0 . 8 667 Ġand Ġ0 . 8 667 Ġrespectively . ĠWe Ġobserve Ġimprovements Ġin Ġthe ĠR OC Ġareas Ġfor Ġboth Ġclasses . ĠThis Ġobservation Ġfurther Ġshows Ġthat Ġour Ġmodel Ġis Ġmore Ġsensitive Ġto Ġall Ġthe Ġclass Ġsamples Ġcompared Ġto Ġthe Ġbase Ġmodel , Ġeven Ġfor Ġthe Ġterms Ġthat Ġhave Ġonly Ġa Ġfew Ġsamples Ġin Ġthe Ġtraining Ġset . ĠAgain , Ġby Ġplotting Ġthe ĠR OC Ġcurves Ġand Ġcomparing ĠA UC Ġareas , Ġwe Ġshow Ġthat Ġour Ġmodel , Ġwhich Ġapplies ĠEL Mo Ġand Ġtopic - att ention , Ġhas Ġa Ġbetter Ġrepresentation Ġability Ġunder Ġthe Ġsetting Ġof Ġfew - shot Ġlearning .
Introduction Medical text mining is an exciting area and is becoming attractive to natural language processing (NLP) researchers. Clinical notes are an example of text in the medical area that recent work has focused on BIBREF0, BIBREF1, BIBREF2. This work studies abbreviation disambiguation on clinical notes BIBREF3, BIBREF4, specifically those used commonly by physicians and nurses. Such clinical abbreviations can have a large number of meanings, depending on the specialty BIBREF5, BIBREF6. For example, the term MR can mean magnetic resonance, mitral regurgitation, mental retardation, medical record and the general English Mister (Mr.). Table TABREF1 illustrates such an example. Abbreviation disambiguation is an important task in medical text understanding task BIBREF7. Successful recognition of the abbreviations in the notes can contribute to downstream tasks such as classification, named entity recognition, and relation extraction BIBREF7. Recent work focuses on formulating the abbreviation disambiguation task as a classification problem, where the possible senses of a given abbreviation term are pre-defined with the help of domain experts BIBREF6, BIBREF5. Traditional features such as part-of-speech (POS) and Term Frequency-Inverse Document Frequency (TF-IDF) are widely investigated for clinical notes classification. Classifiers like support vector machines (SVMs) and random forests (RFs) are used to make predictions BIBREF1. Such methods depend heavily on feature engineering. Recently, deep features have been investigated in the medical domain. Word embeddings BIBREF8 and Convolutional Neural Networks (CNNs) BIBREF9, BIBREF10 provide very competitive performance on text classification for clinical notes and abbreviation disambiguation task BIBREF0, BIBREF11, BIBREF12, BIBREF6. Beyond vanilla embeddings, BIBREF13 utilized contextual features to do abbreviation expansion. Another challenge is the difficulty in obtaining training data: clinical notes have many restrictions due to privacy issues and require domain experts to develop high-quality annotations, thus leading to limited annotated training and testing data. Another difficulty is that in the real world (and in the existing public datasets), some abbreviation term-sense pairs (for example, AB as abortion) have a very high frequency of occurrence BIBREF5, while others are rarely found. This long tail issue creates the challenge of training under unbalanced sample distributions. We tackle these problems in the setting of few-shot learning BIBREF14, BIBREF15 where only a few or a low number of samples can be found in the training dataset to make use of limited resources. We propose a model that combines deep contextual features based on ELMo BIBREF16 and topic information to solve the abbreviation disambiguation task. Our contributions can be summarized as: 1) we re-examined and corrected an existing dataset for training and we collected a test set for evaluation with focus especially for rare senses; 2) we proposed a few-shot learning approach which combines topic information and contextualized word embeddings to solve clinical abbreviation disambiguation task. The implementation is available online; 3) as limited research are conducted on this particular task, we evaluated and compared a number of baseline methods including classical models and deep models comprehensively. Datasets Training Dataset UM Inventory BIBREF5 is a public dataset created by researchers from the University of Minnesota, containing about 37,500 training samples with 75 abbreviation terms. Existing work reports abbreviation disambiguation results on 50 abbreviation terms BIBREF6, BIBREF5, BIBREF17. However, after carefully reviewing this dataset, we found that it contains many samples where medical professionals disagree: wrong samples and uncategorized samples. Due to these mistakes and flaws of this dataset, we removed the erroneous samples and eventually selected 30 abbreviation terms as our training dataset that can be made public. Among all the abbreviation terms, we have 11,466 samples, and 93 term-sense pairs in total (on average 123.3 samples/term-sense pair and 382.2 samples/term). Some term-sense pairs are very popular with a larger number of training samples but some are not (typically less than 5), we call them rare-sense cases . More details can be found in Appendix SECREF7. Testing Dataset Our testing dataset takes MIMIC-III BIBREF18 and PubMed as data sources. Here we are referring to all the notes data from MIMIC-III (NOTEEVENTS table ) and Case Reports articles from PubMed, as this type of contents are close to medical notes. We provide detailed information in Appendix SECREF8, including the steps to create the testing dataset. Eventually, we have a balanced testing dataset, where each term-sense pair has at least 11 and up to 15 samples for training (on average, each pair has 14.56 samples and the median sample number is 15). Baselines We conducted a comprehensive comparison with the baseline models, and some of them were never investigated for the abbreviation disambiguation task. We applied traditional features by simply taking the TF-IDF features as the inputs into the classic classifiers. Deep features are also considered: a Doc2vec model BIBREF19 was pre-trained using Gensim and these word embeddings were applied to initialize deep models and fine-tuned. TF-IDF: We applied TF-IDF features as inputs to four classifiers: support vector machine classifier (SVM), logistic regression classifier (LR), Naive Bayes classifier (NB) and random forest (RF); CNN: We followed the same architecture as BIBREF9; LSTM: We applied an LSTM model BIBREF20 to classify the sentences with pre-trained word embeddings;LSTM-soft: We then added a soft-attention BIBREF21 layer on top of the LSTM model where we computed soft alignment scores over each of the hidden states; LSTM-self: We applied a self-attention layer BIBREF22 to LSTM model. We denote the content vector as $c_i$ for each sentence $i$, as the input to the last classification layer. Topic-attention Model ELMo ELMo is a new type of word representation introduced by BIBREF16 which considers contextual information captured by pre-trained BiLSTM language models. Similar works like BERT BIBREF23 and BioBERT BIBREF24 also consider context but with deeper structures. Compared with those models, ELMo has less parameters and not easy to be overfitting. We trained our ELMo model on the MIMIC-III corpus. Since some sentences also appear in the test set, one may raise the concern of performance inflation in testing. However, we pre-trained ELMo using the whole corpus of MIMIC in an unsupervised way, so the classification labels were not visible during training. To train the ELMo model, we adapted code from AllenNLP, we set the word embedding dimension to 64 and applied two BiLSTM layers. For all of our experiments that involved with ELMo, we initialized the word embeddings from the last layer of ELMo. Topic-attention We propose a neural topic-attention model for text classification. Our assumption is that the topic information plays an important role in doing the disambiguation given a sentence. For example, the abbreviation of the medical term FISH has two potential senses: general English fish as seafood and the sense of fluorescent in situ hybridization BIBREF17. The former case always goes with the topic of food, allergies while the other one appears in the topic of some examination test reports. In our model, a topic-attention module is applied to add topic information into the final sentence representation. This module calculates the distribution of topic-attention weights on a list of topic vectors. As shown in Figure SECREF5, we took the content vector $c_i$ (from soft-attention BIBREF22) and a topic matrix $T_{topic}=[t_1,t_2,..,t_j]$ (where each $t_i$ is a column vector in the figure and we illustrate four topics) as the inputs to the topic-attention module, and then calculated a weighted summation of the topic vectors. The final sentence representation $r_i$ for the sentence $i$ was calculated as the following: where $W_{topic}$ and $b _ { topic }$ are trainable parameters, $\beta _{it}$ represents the topic-attention weights. The final sentence representation is denoted as $r_i$, and $[\cdot ,\cdot ]$ means concatenation. Here $s_i$ is the representation of the sentence which includes the topic information. The final sentence representation $r_i$ is the concatenation of $c_i$ and $s_i$, now we have both context-aware sentence representation and topic-related representation. Then we added a fully-connected layer, followed by a Softmax to do classification with cross-entropy loss function. Topic Matrix To generate the topic matrix $T_{topic}$ as in Equation DISPLAY_FORM9, we propose a convolution-based method to generate topic vectors. We first pre-trained a topic model using the Latent Dirichlet Allocation (LDA) BIBREF25 model on MIMIC-III notes as used by the ELMo model. We set the number of topics to be 50 and ran for 30 iterations. Then we were able to get a list of top $k$ words (we set $k=100$) for each topic. To get the topic vector $t$ for a specific topic: where $e_j$ (column vector) is the pre-trained Doc2vec word embedding for the top word $j$ from the current topic, and $Conv(\cdot )$ indicates a convolutional layer; $maxpool(\cdot )$ is a max pooling layer. We finally reshaped the output $t$ into a 1-dimension vector. Eventually we collected all topic vectors as the topic matrix $T_{topic}$ in Figure SECREF5. Evaluation We did three groups of experiments including two baseline groups and our proposed model. The first group was the TF-IDF features in Section SECREF3 for traditional models. The Naïve Bayesian classifier (NB) has the highest scores among all traditional methods. The second group of experiments used neural models described in Section SECREF3, where LSTM with self attention model (LSTM-self) has competitive results among this group, we choose this model as our base model. Notably, this is the first study that compares and evaluates LSTM-based models on the medical term abbreviation disambiguation task. tableExperimental Results: we report macro F1 in all the experiments. figureTopic-attention Model The last group contains the results of our proposed model with different settings. We used Topic Only setting on top of the base model, where we only added the topic-attention layer, and all the word embeddings were from our pre-trained Doc2vec model and were fine-tuned during back propagation. We could observe that compared with the base model, we have an improvement of 7.36% on accuracy and 9.69% on F1 score. Another model (ELMo Only) is to initialize the word embeddings of the base model using the pre-trained ELMo model, and here no topic information was added. Here we have higher scores than Topic Only, and the accuracy and F1 score increased by 9.87% and 12.26% respectively compared with the base model. We then conducted a combination of both(ELMo+Topic), where the word embeddings from the sentences were computed from the pre-trained ELMo model, and the topic representations were from the pre-trained Doc2vec model. We have a remarkable increase of 12.27% on the accuracy and 14.86% on F1 score. To further compare our proposed topic-attention model and the base model, we report an average of area under the curve(AUC) score among all 30 terms: the base model has an average AUC of 0.7189, and our topic-attention model (ELMo+Topic) achieves an average AUC of 0.8196. We provide a case study in Appendix SECREF9. The results show that the model can benefit from ELMo, which considers contextual features, and the topic-attention module, which brings in topic information. We can conclude that under the few-shot learning setting, our proposed model can better capture the sentence features when only limited training samples are explored in a small-scale dataset. Conclusion In this paper, we propose a neural topic-attention model with few-shot learning for medical abbreviation disambiguation task. We also manually cleaned and collected training and testing data for this task, which we release to promote related research in NLP with clinical notes. In addition, we evaluated and compared a comprehensive set of baseline models, some of which had never been applied to the medical term abbreviation disambiguation task. Future work would be to adapt other models like BioBERT or BERT to our proposed topic-attention model. We will also extend the method into other clinical notes tasks such as drug name recognition and ICD-9 code auto-assigning BIBREF26. In addition, other LDA-based approach can be investigated. Dataset Details Figure FIGREF11 shows the histogram for the distribution of term-sense pair sample numbers. The X-axis gives the pair sample numbers and the Y-axis shows the counts. For example, the first bar shows that there are 43 term-sense pairs that have the sample number in the range of 0-50. We also show histogram of class numbers for all terms in Figure FIGREF11. The Y-axis gives the counts while the X-axis gives the number of classes. For instance, the first bar means there are 12 terms contain 2 classes. Testing Dataset Since the training dataset is unbalanced and relatively small, it is hard to split it further into training and testing. Existing work BIBREF6, BIBREF5 conducted fold validation on the datasets and we found there are extreme rare senses for which only one or two samples exist. Besides, we believe that it is better to evaluate on a balanced dataset to determine whether it performs equally well on all classes. While most of the works deal with unbalanced training and testing that which may lead to very high accuracy if there is a dominating class in both training and testing set, the model may have a poor performance in rare testing cases because only a few samples have been seen during training. To be fair to all the classes, a good performance on these rare cases is also required otherwise it may lead to a severe situation. In this work, we are very interested to see how the model works among all senses especially the rare ones. Also, we can prevent the model from trivially predicting the dominant class and achieving high accuracy. As a result, we decided to create a dataset with the same number of samples for each case in the test dataset. Our testing dataset takes MIMIC-III BIBREF18 and PubMed as data sources. Here we are referring to all the notes data from MIMIC-III (NOTEEVENTS table ) and Case Reports articles from PubMed, as these contents are close to medical notes. To create the test set, we first followed the approach by BIBREF6 who applied an auto-generating method. Initially, we built a term-sense dictionary from the training dataset. Then we did matching for the sense words or phrases in the MIMIC-III notes dataset, and once there is a match, we replaced the words or phrases with the abbreviation term . We then asked two researchers with a medical background to check the matched samples manually with the following judgment: given this sentence and the abbreviation term and its sense, do you think the content is enough for you to guess the sense and whether this is the right sense? To estimate the agreement on the annotations, we selected a subset which contains 120 samples randomly and let the two annotators annotate individually. We got a Kappa score BIBREF27 of 0.96, which is considered as a near perfect agreement. We then distributed the work to the two annotators, and each of them labeled a half of the dataset, which means each sample was only labeled by a single annotator. For some rare term-sense pairs, we failed to find samples from MIMIC-III. The annotators then searched these senses via PubMed data source manually, aiming to find clinical notes-like sentences. They picked good sentences from these results as testing samples where the keywords exist and the content is informative. For those senses that are extremely rare, we let the annotators create sentences in the clinical note style as testing samples according to their experiences. Eventually, we have a balanced testing dataset, where each term-sense pair has around 15 samples for testing (on average, each pair has 14.56 samples and the median sample number is 15), and a comparison with training dataset is shown in Figure FIGREF11. Due to the difficulty for collecting the testing dataset, we decided to only collect for a random selection of 30 terms. On average, it took few hours to generate testing samples for each abbreviation term per researcher. Case Study We now select two representative terms AC, IM and plot their receiver operating characteristic(ROC) curves. The term has a relatively large number of classes and the second one has extremely unbalanced training samples. We show the details in Table TABREF16. We have 8 classes in term AC. Figure FIGREF15(a) illustrates the results of our best performed model and Figure FIGREF15(b) shows the results of the base model. The accuracy and F1 score have an improvement from 0.3898 and 0.2830 to 0.4915 and 0.4059 respectively. Regarding the rare senses (for example, class 0, 1, 4 and 6), we can observe an increase in the ROC areas. Class 6 has an obvious improvement from 0.75 to 1.00. Such improvements in the rare senses make a huge difference in the reported average accuracy and F1 score, since we have a nearly equal number of samples for each class in the testing data. Similarly, we show the plots for IM term in Figure FIGREF15(c) and FIGREF15(d). IM has only two classes, but they are very unbalanced in training set, as shown in Table TABREF16. The accuracy and F1 scores improved from 0.6667 and 0.6250 to 0.8667 and 0.8667 respectively. We observe improvements in the ROC areas for both classes. This observation further shows that our model is more sensitive to all the class samples compared to the base model, even for the terms that have only a few samples in the training set. Again, by plotting the ROC curves and comparing AUC areas, we show that our model, which applies ELMo and topic-attention, has a better representation ability under the setting of few-shot learning.
How big are improvements of small-scale unbalanced datasets when sentence representation is enhanced with topic information?
a712718e6596ba946f29a99838d82f95b9ebb1ce
How Ġbig Ġare Ġimprovements Ġof Ġsmall - scale Ġun balanced Ġdatasets Ġwhen Ġsentence Ġrepresentation Ġis Ġenhanced Ġwith Ġtopic Ġinformation ?
7 . 36 % Ġon Ġaccuracy Ġand Ġ9 . 69 % Ġon ĠF 1 Ġscore
<s> How Ġbig Ġare Ġimprovements Ġof Ġsmall - scale Ġun balanced Ġdatasets Ġwhen Ġsentence Ġrepresentation Ġis Ġenhanced Ġwith Ġtopic Ġinformation ? </s> Introduction </s> Medical Ġtext Ġmining Ġis Ġan Ġexciting Ġarea Ġand Ġis Ġbecoming Ġattractive Ġto Ġnatural Ġlanguage Ġprocessing Ġ( N LP ) Ġresearchers . ĠClinical Ġnotes Ġare Ġan Ġexample Ġof Ġtext Ġin Ġthe Ġmedical Ġarea Ġthat Ġrecent Ġwork Ġhas Ġfocused Ġon ĠB IB REF 0 , ĠB IB REF 1 , ĠB IB REF 2 . ĠThis Ġwork Ġstudies Ġabbre viation Ġdis amb ig uation Ġon Ġclinical Ġnotes ĠB IB REF 3 , ĠB IB REF 4 , Ġspecifically Ġthose Ġused Ġcommonly Ġby Ġphysicians Ġand Ġnurses . ĠSuch Ġclinical Ġabbrevi ations Ġcan Ġhave Ġa Ġlarge Ġnumber Ġof Ġmeanings , Ġdepending Ġon Ġthe Ġspecialty ĠB IB REF 5 , ĠB IB REF 6 . ĠFor Ġexample , Ġthe Ġterm ĠMR Ġcan Ġmean Ġmagnetic Ġresonance , Ġmit ral Ġreg urg itation , Ġmental Ġretard ation , Ġmedical Ġrecord Ġand Ġthe Ġgeneral ĠEnglish ĠMister Ġ( Mr .). ĠTable ĠT AB REF 1 Ġillustrates Ġsuch Ġan Ġexample . ĠAb bre viation Ġdis amb ig uation Ġis Ġan Ġimportant Ġtask Ġin Ġmedical Ġtext Ġunderstanding Ġtask ĠB IB REF 7 . ĠSuccess ful Ġrecognition Ġof Ġthe Ġabbrevi ations Ġin Ġthe Ġnotes Ġcan Ġcontribute Ġto Ġdownstream Ġtasks Ġsuch Ġas Ġclassification , Ġnamed Ġentity Ġrecognition , Ġand Ġrelation Ġextraction ĠB IB REF 7 . </s> Recent Ġwork Ġfocuses Ġon Ġform ulating Ġthe Ġabbre viation Ġdis amb ig uation Ġtask Ġas Ġa Ġclassification Ġproblem , Ġwhere Ġthe Ġpossible Ġsenses Ġof Ġa Ġgiven Ġabbre viation Ġterm Ġare Ġpre - defined Ġwith Ġthe Ġhelp Ġof Ġdomain Ġexperts ĠB IB REF 6 , ĠB IB REF 5 . ĠTraditional Ġfeatures Ġsuch Ġas Ġpart - of - speech Ġ( POS ) Ġand ĠTerm ĠFrequency - In verse ĠDocument ĠFrequency Ġ( TF - ID F ) Ġare Ġwidely Ġinvestigated Ġfor Ġclinical Ġnotes Ġclassification . ĠClass ifiers Ġlike Ġsupport Ġvector Ġmachines Ġ( S V Ms ) Ġand Ġrandom Ġforests Ġ( RF s ) Ġare Ġused Ġto Ġmake Ġpredictions ĠB IB REF 1 . ĠSuch Ġmethods Ġdepend Ġheavily Ġon Ġfeature Ġengineering . ĠRecently , Ġdeep Ġfeatures Ġhave Ġbeen Ġinvestigated Ġin Ġthe Ġmedical Ġdomain . ĠWord Ġembed d ings ĠB IB REF 8 Ġand ĠConv olution al ĠNeural ĠNetworks Ġ( CNN s ) ĠB IB REF 9 , ĠB IB REF 10 Ġprovide Ġvery Ġcompetitive Ġperformance Ġon Ġtext Ġclassification Ġfor Ġclinical Ġnotes Ġand Ġabbre viation Ġdis amb ig uation Ġtask ĠB IB REF 0 , ĠB IB REF 11 , ĠB IB REF 12 , ĠB IB REF 6 . ĠBeyond Ġvanilla Ġembed d ings , ĠB IB REF 13 Ġutilized Ġcontextual Ġfeatures Ġto Ġdo Ġabbre viation Ġexpansion . ĠAnother Ġchallenge Ġis Ġthe Ġdifficulty Ġin Ġobtaining Ġtraining Ġdata : Ġclinical Ġnotes Ġhave Ġmany Ġrestrictions Ġdue Ġto Ġprivacy Ġissues Ġand Ġrequire Ġdomain Ġexperts Ġto Ġdevelop Ġhigh - quality Ġannotations , Ġthus Ġleading Ġto Ġlimited Ġannot ated Ġtraining Ġand Ġtesting Ġdata . ĠAnother Ġdifficulty Ġis Ġthat Ġin Ġthe Ġreal Ġworld Ġ( and Ġin Ġthe Ġexisting Ġpublic Ġdatasets ), Ġsome Ġabbre viation Ġterm - sense Ġpairs Ġ( for Ġexample , ĠAB Ġas Ġabortion ) Ġhave Ġa Ġvery Ġhigh Ġfrequency Ġof Ġoccurrence ĠB IB REF 5 , Ġwhile Ġothers Ġare Ġrarely Ġfound . ĠThis Ġlong Ġtail Ġissue Ġcreates Ġthe Ġchallenge Ġof Ġtraining Ġunder Ġun balanced Ġsample Ġdistributions . ĠWe Ġtackle Ġthese Ġproblems Ġin Ġthe Ġsetting Ġof Ġfew - shot Ġlearning ĠB IB REF 14 , ĠB IB REF 15 Ġwhere Ġonly Ġa Ġfew Ġor Ġa Ġlow Ġnumber Ġof Ġsamples Ġcan Ġbe Ġfound Ġin Ġthe Ġtraining Ġdataset Ġto Ġmake Ġuse Ġof Ġlimited Ġresources . ĠWe Ġpropose Ġa Ġmodel Ġthat Ġcombines Ġdeep Ġcontextual Ġfeatures Ġbased Ġon ĠEL Mo ĠB IB REF 16 Ġand Ġtopic Ġinformation Ġto Ġsolve Ġthe Ġabbre viation Ġdis amb ig uation Ġtask . </s> Our Ġcontributions Ġcan Ġbe Ġsummarized Ġas : Ġ1 ) Ġwe Ġre - ex am ined Ġand Ġcorrected Ġan Ġexisting Ġdataset Ġfor Ġtraining Ġand Ġwe Ġcollected Ġa Ġtest Ġset Ġfor Ġevaluation Ġwith Ġfocus Ġespecially Ġfor Ġrare Ġsenses ; Ġ2 ) Ġwe Ġproposed Ġa Ġfew - shot Ġlearning Ġapproach Ġwhich Ġcombines Ġtopic Ġinformation Ġand Ġcontextual ized Ġword Ġembed d ings Ġto Ġsolve Ġclinical Ġabbre viation Ġdis amb ig uation Ġtask . ĠThe Ġimplementation Ġis Ġavailable Ġonline ; Ġ3 ) Ġas Ġlimited Ġresearch Ġare Ġconducted Ġon Ġthis Ġparticular Ġtask , Ġwe Ġevaluated Ġand Ġcompared Ġa Ġnumber Ġof Ġbaseline Ġmethods Ġincluding Ġclassical Ġmodels Ġand Ġdeep Ġmodels Ġcomprehens ively . </s> Dat as ets </s> Training ĠDat as et ĠUM ĠInventory ĠB IB REF 5 Ġis Ġa Ġpublic Ġdataset Ġcreated Ġby Ġresearchers Ġfrom Ġthe ĠUniversity Ġof ĠMinnesota , Ġcontaining Ġabout Ġ37 , 500 Ġtraining Ġsamples Ġwith Ġ75 Ġabbre viation Ġterms . ĠEx isting Ġwork Ġreports Ġabbre viation Ġdis amb ig uation Ġresults Ġon Ġ50 Ġabbre viation Ġterms ĠB IB REF 6 , ĠB IB REF 5 , ĠB IB REF 17 . ĠHowever , Ġafter Ġcarefully Ġreviewing Ġthis Ġdataset , Ġwe Ġfound Ġthat Ġit Ġcontains Ġmany Ġsamples Ġwhere Ġmedical Ġprofessionals Ġdisagree : Ġwrong Ġsamples Ġand Ġunc ategor ized Ġsamples . ĠDue Ġto Ġthese Ġmistakes Ġand Ġflaws Ġof Ġthis Ġdataset , Ġwe Ġremoved Ġthe Ġerroneous Ġsamples Ġand Ġeventually Ġselected Ġ30 Ġabbre viation Ġterms Ġas Ġour Ġtraining Ġdataset Ġthat Ġcan Ġbe Ġmade Ġpublic . ĠAmong Ġall Ġthe Ġabbre viation Ġterms , Ġwe Ġhave Ġ11 , 466 Ġsamples , Ġand Ġ93 Ġterm - sense Ġpairs Ġin Ġtotal Ġ( on Ġaverage Ġ123 . 3 Ġsamples / term - sense Ġpair Ġand Ġ38 2 . 2 Ġsamples / term ). ĠSome Ġterm - sense Ġpairs Ġare Ġvery Ġpopular Ġwith Ġa Ġlarger Ġnumber Ġof Ġtraining Ġsamples Ġbut Ġsome Ġare Ġnot Ġ( typically Ġless Ġthan Ġ5 ), Ġwe Ġcall Ġthem Ġrare - sense Ġcases Ġ. ĠMore Ġdetails Ġcan Ġbe Ġfound Ġin ĠAppendix ĠSEC REF 7 . </s> Testing ĠDat as et ĠOur Ġtesting Ġdataset Ġtakes ĠM IM IC - III ĠB IB REF 18 Ġand ĠPubMed Ġas Ġdata Ġsources . ĠHere Ġwe Ġare Ġreferring Ġto Ġall Ġthe Ġnotes Ġdata Ġfrom ĠM IM IC - III Ġ( NOT EE V ENTS Ġtable Ġ) Ġand ĠCase ĠReports Ġarticles Ġfrom ĠPubMed , Ġas Ġthis Ġtype Ġof Ġcontents Ġare Ġclose Ġto Ġmedical Ġnotes . ĠWe Ġprovide Ġdetailed Ġinformation Ġin ĠAppendix ĠSEC REF 8 , Ġincluding Ġthe Ġsteps Ġto Ġcreate Ġthe Ġtesting Ġdataset . ĠEventually , Ġwe Ġhave Ġa Ġbalanced Ġtesting Ġdataset , Ġwhere Ġeach Ġterm - sense Ġpair Ġhas Ġat Ġleast Ġ11 Ġand Ġup Ġto Ġ15 Ġsamples Ġfor Ġtraining Ġ( on Ġaverage , Ġeach Ġpair Ġhas Ġ14 . 56 Ġsamples Ġand Ġthe Ġmedian Ġsample Ġnumber Ġis Ġ15 ). </s> Bas elines </s> We Ġconducted Ġa Ġcomprehensive Ġcomparison Ġwith Ġthe Ġbaseline Ġmodels , Ġand Ġsome Ġof Ġthem Ġwere Ġnever Ġinvestigated Ġfor Ġthe Ġabbre viation Ġdis amb ig uation Ġtask . ĠWe Ġapplied Ġtraditional Ġfeatures Ġby Ġsimply Ġtaking Ġthe ĠTF - ID F Ġfeatures Ġas Ġthe Ġinputs Ġinto Ġthe Ġclassic Ġclass ifiers . ĠDeep Ġfeatures Ġare Ġalso Ġconsidered : Ġa ĠDoc 2 vec Ġmodel ĠB IB REF 19 Ġwas Ġpre - trained Ġusing ĠG ens im Ġand Ġthese Ġword Ġembed d ings Ġwere Ġapplied Ġto Ġinitialize Ġdeep Ġmodels Ġand Ġfine - tun ed . </s> TF - ID F : ĠWe Ġapplied ĠTF - ID F Ġfeatures Ġas Ġinputs Ġto Ġfour Ġclass ifiers : Ġsupport Ġvector Ġmachine Ġclass ifier Ġ( S VM ), Ġlog istic Ġregression Ġclass ifier Ġ( LR ), ĠNa ive ĠBay es Ġclass ifier Ġ( NB ) Ġand Ġrandom Ġforest Ġ( RF ); ĠCNN : ĠWe Ġfollowed Ġthe Ġsame Ġarchitecture Ġas ĠB IB REF 9 ; ĠL ST M : ĠWe Ġapplied Ġan ĠL ST M Ġmodel ĠB IB REF 20 Ġto Ġclassify Ġthe Ġsentences Ġwith Ġpre - trained Ġword Ġembed d ings ; L ST M - soft : ĠWe Ġthen Ġadded Ġa Ġsoft - att ention ĠB IB REF 21 Ġlayer Ġon Ġtop Ġof Ġthe ĠL ST M Ġmodel Ġwhere Ġwe Ġcomputed Ġsoft Ġalignment Ġscores Ġover Ġeach Ġof Ġthe Ġhidden Ġstates ; ĠL ST M - self : ĠWe Ġapplied Ġa Ġself - att ention Ġlayer ĠB IB REF 22 Ġto ĠL ST M Ġmodel . ĠWe Ġdenote Ġthe Ġcontent Ġvector Ġas Ġ$ c _ i $ Ġfor Ġeach Ġsentence Ġ$ i $, Ġas Ġthe Ġinput Ġto Ġthe Ġlast Ġclassification Ġlayer . </s> Topic - att ention ĠModel </s> EL Mo ĠEL Mo Ġis Ġa Ġnew Ġtype Ġof Ġword Ġrepresentation Ġintroduced Ġby ĠB IB REF 16 Ġwhich Ġconsiders Ġcontextual Ġinformation Ġcaptured Ġby Ġpre - trained ĠBi L ST M Ġlanguage Ġmodels . ĠSimilar Ġworks Ġlike ĠB ERT ĠB IB REF 23 Ġand ĠBio BER T ĠB IB REF 24 Ġalso Ġconsider Ġcontext Ġbut Ġwith Ġdeeper Ġstructures . ĠCompared Ġwith Ġthose Ġmodels , ĠEL Mo Ġhas Ġless Ġparameters Ġand Ġnot Ġeasy Ġto Ġbe Ġover fitting . ĠWe Ġtrained Ġour ĠEL Mo Ġmodel Ġon Ġthe ĠM IM IC - III Ġcorpus . ĠSince Ġsome Ġsentences Ġalso Ġappear Ġin Ġthe Ġtest Ġset , Ġone Ġmay Ġraise Ġthe Ġconcern Ġof Ġperformance Ġinflation Ġin Ġtesting . ĠHowever , Ġwe Ġpre - trained ĠEL Mo Ġusing Ġthe Ġwhole Ġcorpus Ġof ĠM IM IC Ġin Ġan Ġun super vised Ġway , Ġso Ġthe Ġclassification Ġlabels Ġwere Ġnot Ġvisible Ġduring Ġtraining . ĠTo Ġtrain Ġthe ĠEL Mo Ġmodel , Ġwe Ġadapted Ġcode Ġfrom ĠAllen N LP , Ġwe Ġset Ġthe Ġword Ġembed ding Ġdimension Ġto Ġ64 Ġand Ġapplied Ġtwo ĠBi L ST M Ġlayers . ĠFor Ġall Ġof Ġour Ġexperiments Ġthat Ġinvolved Ġwith ĠEL Mo , Ġwe Ġinitialized Ġthe Ġword Ġembed d ings Ġfrom Ġthe Ġlast Ġlayer Ġof ĠEL Mo . </s> Topic - att ention ĠWe Ġpropose Ġa Ġneural Ġtopic - att ention Ġmodel Ġfor Ġtext Ġclassification . ĠOur Ġassumption Ġis Ġthat Ġthe Ġtopic Ġinformation Ġplays Ġan Ġimportant Ġrole Ġin Ġdoing Ġthe Ġdis amb ig uation Ġgiven Ġa Ġsentence . ĠFor Ġexample , Ġthe Ġabbre viation Ġof Ġthe Ġmedical Ġterm ĠF ISH Ġhas Ġtwo Ġpotential Ġsenses : Ġgeneral ĠEnglish Ġfish Ġas Ġseafood Ġand Ġthe Ġsense Ġof Ġfluorescent Ġin Ġsitu Ġhybrid ization ĠB IB REF 17 . ĠThe Ġformer Ġcase Ġalways Ġgoes Ġwith Ġthe Ġtopic Ġof Ġfood , Ġallergies Ġwhile Ġthe Ġother Ġone Ġappears Ġin Ġthe Ġtopic Ġof Ġsome Ġexamination Ġtest Ġreports . ĠIn Ġour Ġmodel , Ġa Ġtopic - att ention Ġmodule Ġis Ġapplied Ġto Ġadd Ġtopic Ġinformation Ġinto Ġthe Ġfinal Ġsentence Ġrepresentation . ĠThis Ġmodule Ġcalculates Ġthe Ġdistribution Ġof Ġtopic - att ention Ġweights Ġon Ġa Ġlist Ġof Ġtopic Ġvectors . ĠAs Ġshown Ġin ĠFigure ĠSEC REF 5 , Ġwe Ġtook Ġthe Ġcontent Ġvector Ġ$ c _ i $ Ġ( from Ġsoft - att ention ĠB IB REF 22 ) Ġand Ġa Ġtopic Ġmatrix Ġ$ T _{ topic } =[ t _ 1 , t _ 2 , .. , t _ j ] $ Ġ( where Ġeach Ġ$ t _ i $ Ġis Ġa Ġcolumn Ġvector Ġin Ġthe Ġfigure Ġand Ġwe Ġillustrate Ġfour Ġtopics ) Ġas Ġthe Ġinputs Ġto Ġthe Ġtopic - att ention Ġmodule , Ġand Ġthen Ġcalculated Ġa Ġweighted Ġsumm ation Ġof Ġthe Ġtopic Ġvectors . ĠThe Ġfinal Ġsentence Ġrepresentation Ġ$ r _ i $ Ġfor Ġthe Ġsentence Ġ$ i $ Ġwas Ġcalculated Ġas Ġthe Ġfollowing : </s> where Ġ$ W _{ topic } $ Ġand Ġ$ b Ġ_ Ġ{ Ġtopic Ġ} $ Ġare Ġtrain able Ġparameters , Ġ$\ beta Ġ_ { it } $ Ġrepresents Ġthe Ġtopic - att ention Ġweights . ĠThe Ġfinal Ġsentence Ġrepresentation Ġis Ġden oted Ġas Ġ$ r _ i $, Ġand Ġ$ [ \ cd ot Ġ, \ cd ot Ġ] $ Ġmeans Ġconc aten ation . ĠHere Ġ$ s _ i $ Ġis Ġthe Ġrepresentation Ġof Ġthe Ġsentence Ġwhich Ġincludes Ġthe Ġtopic Ġinformation . ĠThe Ġfinal Ġsentence Ġrepresentation Ġ$ r _ i $ Ġis Ġthe Ġconc aten ation Ġof Ġ$ c _ i $ Ġand Ġ$ s _ i $, Ġnow Ġwe Ġhave Ġboth Ġcontext - aware Ġsentence Ġrepresentation Ġand Ġtopic - related Ġrepresentation . ĠThen Ġwe Ġadded Ġa Ġfully - connected Ġlayer , Ġfollowed Ġby Ġa ĠSoft max Ġto Ġdo Ġclassification Ġwith Ġcross - ent ropy Ġloss Ġfunction . </s> Topic ĠMatrix ĠTo Ġgenerate Ġthe Ġtopic Ġmatrix Ġ$ T _{ topic } $ Ġas Ġin ĠEqu ation ĠDIS PLAY _ FORM 9 , Ġwe Ġpropose Ġa Ġconv olution - based Ġmethod Ġto Ġgenerate Ġtopic Ġvectors . ĠWe Ġfirst Ġpre - trained Ġa Ġtopic Ġmodel Ġusing Ġthe ĠLat ent ĠDir ich let ĠAll ocation Ġ( L DA ) ĠB IB REF 25 Ġmodel Ġon ĠM IM IC - III Ġnotes Ġas Ġused Ġby Ġthe ĠEL Mo Ġmodel . ĠWe Ġset Ġthe Ġnumber Ġof Ġtopics Ġto Ġbe Ġ50 Ġand Ġran Ġfor Ġ30 Ġiterations . ĠThen Ġwe Ġwere Ġable Ġto Ġget Ġa Ġlist Ġof Ġtop Ġ$ k $ Ġwords Ġ( we Ġset Ġ$ k = 100 $ ) Ġfor Ġeach Ġtopic . ĠTo Ġget Ġthe Ġtopic Ġvector Ġ$ t $ Ġfor Ġa Ġspecific Ġtopic : </s> where Ġ$ e _ j $ Ġ( column Ġvector ) Ġis Ġthe Ġpre - trained ĠDoc 2 vec Ġword Ġembed ding Ġfor Ġthe Ġtop Ġword Ġ$ j $ Ġfrom Ġthe Ġcurrent Ġtopic , Ġand Ġ$ Con v (\ cd ot Ġ) $ Ġindicates Ġa Ġconv olution al Ġlayer ; Ġ$ max pool (\ cd ot Ġ) $ Ġis Ġa Ġmax Ġpool ing Ġlayer . ĠWe Ġfinally Ġresh aped Ġthe Ġoutput Ġ$ t $ Ġinto Ġa Ġ1 - dimension Ġvector . ĠEventually Ġwe Ġcollected Ġall Ġtopic Ġvectors Ġas Ġthe Ġtopic Ġmatrix Ġ$ T _{ topic } $ Ġin ĠFigure ĠSEC REF 5 . </s> E val uation </s> We Ġdid Ġthree Ġgroups Ġof Ġexperiments Ġincluding Ġtwo Ġbaseline Ġgroups Ġand Ġour Ġproposed Ġmodel . ĠThe Ġfirst Ġgroup Ġwas Ġthe ĠTF - ID F Ġfeatures Ġin ĠSection ĠSEC REF 3 Ġfor Ġtraditional Ġmodels . ĠThe ĠNa Ã¯ve ĠBay esian Ġclass ifier Ġ( NB ) Ġhas Ġthe Ġhighest Ġscores Ġamong Ġall Ġtraditional Ġmethods . ĠThe Ġsecond Ġgroup Ġof Ġexperiments Ġused Ġneural Ġmodels Ġdescribed Ġin ĠSection ĠSEC REF 3 , Ġwhere ĠL ST M Ġwith Ġself Ġattention Ġmodel Ġ( L ST M - self ) Ġhas Ġcompetitive Ġresults Ġamong Ġthis Ġgroup , Ġwe Ġchoose Ġthis Ġmodel Ġas Ġour Ġbase Ġmodel . ĠNotably , Ġthis Ġis Ġthe Ġfirst Ġstudy Ġthat Ġcompares Ġand Ġevaluates ĠL ST M - based Ġmodels Ġon Ġthe Ġmedical Ġterm Ġabbre viation Ġdis amb ig uation Ġtask . </s> table Exper imental ĠResults : Ġwe Ġreport Ġmacro ĠF 1 Ġin Ġall Ġthe Ġexperiments . Ġfigure Topic - att ention ĠModel </s> The Ġlast Ġgroup Ġcontains Ġthe Ġresults Ġof Ġour Ġproposed Ġmodel Ġwith Ġdifferent Ġsettings . ĠWe Ġused ĠTopic ĠOnly Ġsetting Ġon Ġtop Ġof Ġthe Ġbase Ġmodel , Ġwhere Ġwe Ġonly Ġadded Ġthe Ġtopic - att ention Ġlayer , Ġand Ġall Ġthe Ġword Ġembed d ings Ġwere Ġfrom Ġour Ġpre - trained ĠDoc 2 vec Ġmodel Ġand Ġwere Ġfine - tun ed Ġduring Ġback Ġpropagation . ĠWe Ġcould Ġobserve Ġthat Ġcompared Ġwith Ġthe Ġbase Ġmodel , Ġwe Ġhave Ġan Ġimprovement Ġof Ġ7 . 36 % Ġon Ġaccuracy Ġand Ġ9 . 69 % Ġon ĠF 1 Ġscore . ĠAnother Ġmodel Ġ( EL Mo ĠOnly ) Ġis Ġto Ġinitialize Ġthe Ġword Ġembed d ings Ġof Ġthe Ġbase Ġmodel Ġusing Ġthe Ġpre - trained ĠEL Mo Ġmodel , Ġand Ġhere Ġno Ġtopic Ġinformation Ġwas Ġadded . ĠHere Ġwe Ġhave Ġhigher Ġscores Ġthan ĠTopic ĠOnly , Ġand Ġthe Ġaccuracy Ġand ĠF 1 Ġscore Ġincreased Ġby Ġ9 . 87 % Ġand Ġ12 . 26 % Ġrespectively Ġcompared Ġwith Ġthe Ġbase Ġmodel . ĠWe Ġthen Ġconducted Ġa Ġcombination Ġof Ġboth ( EL Mo + Topic ), Ġwhere Ġthe Ġword Ġembed d ings Ġfrom Ġthe Ġsentences Ġwere Ġcomputed Ġfrom Ġthe Ġpre - trained ĠEL Mo Ġmodel , Ġand Ġthe Ġtopic Ġrepresentations Ġwere Ġfrom Ġthe Ġpre - trained ĠDoc 2 vec Ġmodel . ĠWe Ġhave Ġa Ġremarkable Ġincrease Ġof Ġ12 . 27 % Ġon Ġthe Ġaccuracy Ġand Ġ14 . 86 % Ġon ĠF 1 Ġscore . </s> To Ġfurther Ġcompare Ġour Ġproposed Ġtopic - att ention Ġmodel Ġand Ġthe Ġbase Ġmodel , Ġwe Ġreport Ġan Ġaverage Ġof Ġarea Ġunder Ġthe Ġcurve ( A UC ) Ġscore Ġamong Ġall Ġ30 Ġterms : Ġthe Ġbase Ġmodel Ġhas Ġan Ġaverage ĠA UC Ġof Ġ0 . 7 189 , Ġand Ġour Ġtopic - att ention Ġmodel Ġ( EL Mo + Topic ) Ġachieves Ġan Ġaverage ĠA UC Ġof Ġ0 . 8 196 . ĠWe Ġprovide Ġa Ġcase Ġstudy Ġin ĠAppendix ĠSEC REF 9 . ĠThe Ġresults Ġshow Ġthat Ġthe Ġmodel Ġcan Ġbenefit Ġfrom ĠEL Mo , Ġwhich Ġconsiders Ġcontextual Ġfeatures , Ġand Ġthe Ġtopic - att ention Ġmodule , Ġwhich Ġbrings Ġin Ġtopic Ġinformation . ĠWe Ġcan Ġconclude Ġthat Ġunder Ġthe Ġfew - shot Ġlearning Ġsetting , Ġour Ġproposed Ġmodel Ġcan Ġbetter Ġcapture Ġthe Ġsentence Ġfeatures Ġwhen Ġonly Ġlimited Ġtraining Ġsamples Ġare Ġexplored Ġin Ġa Ġsmall - scale Ġdataset . </s> Conclusion </s> In Ġthis Ġpaper , Ġwe Ġpropose Ġa Ġneural Ġtopic - att ention Ġmodel Ġwith Ġfew - shot Ġlearning Ġfor Ġmedical Ġabbre viation Ġdis amb ig uation Ġtask . ĠWe Ġalso Ġmanually Ġcleaned Ġand Ġcollected Ġtraining Ġand Ġtesting Ġdata Ġfor Ġthis Ġtask , Ġwhich Ġwe Ġrelease Ġto Ġpromote Ġrelated Ġresearch Ġin ĠN LP Ġwith Ġclinical Ġnotes . ĠIn Ġaddition , Ġwe Ġevaluated Ġand Ġcompared Ġa Ġcomprehensive Ġset Ġof Ġbaseline Ġmodels , Ġsome Ġof Ġwhich Ġhad Ġnever Ġbeen Ġapplied Ġto Ġthe Ġmedical Ġterm Ġabbre viation Ġdis amb ig uation Ġtask . ĠFuture Ġwork Ġwould Ġbe Ġto Ġadapt Ġother Ġmodels Ġlike ĠBio BER T Ġor ĠB ERT Ġto Ġour Ġproposed Ġtopic - att ention Ġmodel . ĠWe Ġwill Ġalso Ġextend Ġthe Ġmethod Ġinto Ġother Ġclinical Ġnotes Ġtasks Ġsuch Ġas Ġdrug Ġname Ġrecognition Ġand ĠI CD - 9 Ġcode Ġauto - ass igning ĠB IB REF 26 . ĠIn Ġaddition , Ġother ĠL DA - based Ġapproach Ġcan Ġbe Ġinvestigated . </s> Dat as et ĠDetails </s> Figure ĠFIG REF 11 Ġshows Ġthe Ġhist ogram Ġfor Ġthe Ġdistribution Ġof Ġterm - sense Ġpair Ġsample Ġnumbers . ĠThe ĠX - axis Ġgives Ġthe Ġpair Ġsample Ġnumbers Ġand Ġthe ĠY - axis Ġshows Ġthe Ġcounts . ĠFor Ġexample , Ġthe Ġfirst Ġbar Ġshows Ġthat Ġthere Ġare Ġ43 Ġterm - sense Ġpairs Ġthat Ġhave Ġthe Ġsample Ġnumber Ġin Ġthe Ġrange Ġof Ġ0 - 50 . </s> We Ġalso Ġshow Ġhist ogram Ġof Ġclass Ġnumbers Ġfor Ġall Ġterms Ġin ĠFigure ĠFIG REF 11 . ĠThe ĠY - axis Ġgives Ġthe Ġcounts Ġwhile Ġthe ĠX - axis Ġgives Ġthe Ġnumber Ġof Ġclasses . ĠFor Ġinstance , Ġthe Ġfirst Ġbar Ġmeans Ġthere Ġare Ġ12 Ġterms Ġcontain Ġ2 Ġclasses . </s> Testing ĠDat as et </s> Since Ġthe Ġtraining Ġdataset Ġis Ġun balanced Ġand Ġrelatively Ġsmall , Ġit Ġis Ġhard Ġto Ġsplit Ġit Ġfurther Ġinto Ġtraining Ġand Ġtesting . ĠEx isting Ġwork ĠB IB REF 6 , ĠB IB REF 5 Ġconducted Ġfold Ġvalidation Ġon Ġthe Ġdatasets Ġand Ġwe Ġfound Ġthere Ġare Ġextreme Ġrare Ġsenses Ġfor Ġwhich Ġonly Ġone Ġor Ġtwo Ġsamples Ġexist . ĠBesides , Ġwe Ġbelieve Ġthat Ġit Ġis Ġbetter Ġto Ġevaluate Ġon Ġa Ġbalanced Ġdataset Ġto Ġdetermine Ġwhether Ġit Ġperforms Ġequally Ġwell Ġon Ġall Ġclasses . ĠWhile Ġmost Ġof Ġthe Ġworks Ġdeal Ġwith Ġun balanced Ġtraining Ġand Ġtesting Ġthat Ġwhich Ġmay Ġlead Ġto Ġvery Ġhigh Ġaccuracy Ġif Ġthere Ġis Ġa Ġdominating Ġclass Ġin Ġboth Ġtraining Ġand Ġtesting Ġset , Ġthe Ġmodel Ġmay Ġhave Ġa Ġpoor Ġperformance Ġin Ġrare Ġtesting Ġcases Ġbecause Ġonly Ġa Ġfew Ġsamples Ġhave Ġbeen Ġseen Ġduring Ġtraining . ĠTo Ġbe Ġfair Ġto Ġall Ġthe Ġclasses , Ġa Ġgood Ġperformance Ġon Ġthese Ġrare Ġcases Ġis Ġalso Ġrequired Ġotherwise Ġit Ġmay Ġlead Ġto Ġa Ġsevere Ġsituation . ĠIn Ġthis Ġwork , Ġwe Ġare Ġvery Ġinterested Ġto Ġsee Ġhow Ġthe Ġmodel Ġworks Ġamong Ġall Ġsenses Ġespecially Ġthe Ġrare Ġones . ĠAlso , Ġwe Ġcan Ġprevent Ġthe Ġmodel Ġfrom Ġtriv ially Ġpredicting Ġthe Ġdominant Ġclass Ġand Ġachieving Ġhigh Ġaccuracy . ĠAs Ġa Ġresult , Ġwe Ġdecided Ġto Ġcreate Ġa Ġdataset Ġwith Ġthe Ġsame Ġnumber Ġof Ġsamples Ġfor Ġeach Ġcase Ġin Ġthe Ġtest Ġdataset . </s> Our Ġtesting Ġdataset Ġtakes ĠM IM IC - III ĠB IB REF 18 Ġand ĠPubMed Ġas Ġdata Ġsources . ĠHere Ġwe Ġare Ġreferring Ġto Ġall Ġthe Ġnotes Ġdata Ġfrom ĠM IM IC - III Ġ( NOT EE V ENTS Ġtable Ġ) Ġand ĠCase ĠReports Ġarticles Ġfrom ĠPubMed , Ġas Ġthese Ġcontents Ġare Ġclose Ġto Ġmedical Ġnotes . ĠTo Ġcreate Ġthe Ġtest Ġset , Ġwe Ġfirst Ġfollowed Ġthe Ġapproach Ġby ĠB IB REF 6 Ġwho Ġapplied Ġan Ġauto - gener ating Ġmethod . ĠInitially , Ġwe Ġbuilt Ġa Ġterm - sense Ġdictionary Ġfrom Ġthe Ġtraining Ġdataset . ĠThen Ġwe Ġdid Ġmatching Ġfor Ġthe Ġsense Ġwords Ġor Ġphrases Ġin Ġthe ĠM IM IC - III Ġnotes Ġdataset , Ġand Ġonce Ġthere Ġis Ġa Ġmatch , Ġwe Ġreplaced Ġthe Ġwords Ġor Ġphrases Ġwith Ġthe Ġabbre viation Ġterm Ġ. ĠWe Ġthen Ġasked Ġtwo Ġresearchers Ġwith Ġa Ġmedical Ġbackground Ġto Ġcheck Ġthe Ġmatched Ġsamples Ġmanually Ġwith Ġthe Ġfollowing Ġjudgment : Ġgiven Ġthis Ġsentence Ġand Ġthe Ġabbre viation Ġterm Ġand Ġits Ġsense , Ġdo Ġyou Ġthink Ġthe Ġcontent Ġis Ġenough Ġfor Ġyou Ġto Ġguess Ġthe Ġsense Ġand Ġwhether Ġthis Ġis Ġthe Ġright Ġsense ? ĠTo Ġestimate Ġthe Ġagreement Ġon Ġthe Ġannotations , Ġwe Ġselected Ġa Ġsubset Ġwhich Ġcontains Ġ120 Ġsamples Ġrandomly Ġand Ġlet Ġthe Ġtwo Ġannot ators Ġannot ate Ġindividually . ĠWe Ġgot Ġa ĠKappa Ġscore ĠB IB REF 27 Ġof Ġ0 . 96 , Ġwhich Ġis Ġconsidered Ġas Ġa Ġnear Ġperfect Ġagreement . ĠWe Ġthen Ġdistributed Ġthe Ġwork Ġto Ġthe Ġtwo Ġannot ators , Ġand Ġeach Ġof Ġthem Ġlabeled Ġa Ġhalf Ġof Ġthe Ġdataset , Ġwhich Ġmeans Ġeach Ġsample Ġwas Ġonly Ġlabeled Ġby Ġa Ġsingle Ġannot ator . ĠFor Ġsome Ġrare Ġterm - sense Ġpairs , Ġwe Ġfailed Ġto Ġfind Ġsamples Ġfrom ĠM IM IC - III . ĠThe Ġannot ators Ġthen Ġsearched Ġthese Ġsenses Ġvia ĠPubMed Ġdata Ġsource Ġmanually , Ġaiming Ġto Ġfind Ġclinical Ġnotes - like Ġsentences . ĠThey Ġpicked Ġgood Ġsentences Ġfrom Ġthese Ġresults Ġas Ġtesting Ġsamples Ġwhere Ġthe Ġkeywords Ġexist Ġand Ġthe Ġcontent Ġis Ġinformative . ĠFor Ġthose Ġsenses Ġthat Ġare Ġextremely Ġrare , Ġwe Ġlet Ġthe Ġannot ators Ġcreate Ġsentences Ġin Ġthe Ġclinical Ġnote Ġstyle Ġas Ġtesting Ġsamples Ġaccording Ġto Ġtheir Ġexperiences . ĠEventually , Ġwe Ġhave Ġa Ġbalanced Ġtesting Ġdataset , Ġwhere Ġeach Ġterm - sense Ġpair Ġhas Ġaround Ġ15 Ġsamples Ġfor Ġtesting Ġ( on Ġaverage , Ġeach Ġpair Ġhas Ġ14 . 56 Ġsamples Ġand Ġthe Ġmedian Ġsample Ġnumber Ġis Ġ15 ), Ġand Ġa Ġcomparison Ġwith Ġtraining Ġdataset Ġis Ġshown Ġin ĠFigure ĠFIG REF 11 . ĠDue Ġto Ġthe Ġdifficulty Ġfor Ġcollecting Ġthe Ġtesting Ġdataset , Ġwe Ġdecided Ġto Ġonly Ġcollect Ġfor Ġa Ġrandom Ġselection Ġof Ġ30 Ġterms . ĠOn Ġaverage , Ġit Ġtook Ġfew Ġhours Ġto Ġgenerate Ġtesting Ġsamples Ġfor Ġeach Ġabbre viation Ġterm Ġper Ġresearcher . </s> Case ĠStudy </s> We Ġnow Ġselect Ġtwo Ġrepresentative Ġterms ĠAC , ĠIM Ġand Ġplot Ġtheir Ġreceiver Ġoperating Ġcharacteristic ( R OC ) Ġcurves . ĠThe Ġterm Ġhas Ġa Ġrelatively Ġlarge Ġnumber Ġof Ġclasses Ġand Ġthe Ġsecond Ġone Ġhas Ġextremely Ġun balanced Ġtraining Ġsamples . ĠWe Ġshow Ġthe Ġdetails Ġin ĠTable ĠT AB REF 16 . ĠWe Ġhave Ġ8 Ġclasses Ġin Ġterm ĠAC . ĠFigure ĠFIG REF 15 ( a ) Ġillustrates Ġthe Ġresults Ġof Ġour Ġbest Ġperformed Ġmodel Ġand ĠFigure ĠFIG REF 15 ( b ) Ġshows Ġthe Ġresults Ġof Ġthe Ġbase Ġmodel . ĠThe Ġaccuracy Ġand ĠF 1 Ġscore Ġhave Ġan Ġimprovement Ġfrom Ġ0 . 38 98 Ġand Ġ0 . 28 30 Ġto Ġ0 . 49 15 Ġand Ġ0 . 40 59 Ġrespectively . ĠRegarding Ġthe Ġrare Ġsenses Ġ( for Ġexample , Ġclass Ġ0 , Ġ1 , Ġ4 Ġand Ġ6 ), Ġwe Ġcan Ġobserve Ġan Ġincrease Ġin Ġthe ĠR OC Ġareas . ĠClass Ġ6 Ġhas Ġan Ġobvious Ġimprovement Ġfrom Ġ0 . 75 Ġto Ġ1 . 00 . ĠSuch Ġimprovements Ġin Ġthe Ġrare Ġsenses Ġmake Ġa Ġhuge Ġdifference Ġin Ġthe Ġreported Ġaverage Ġaccuracy Ġand ĠF 1 Ġscore , Ġsince Ġwe Ġhave Ġa Ġnearly Ġequal Ġnumber Ġof Ġsamples Ġfor Ġeach Ġclass Ġin Ġthe Ġtesting Ġdata . ĠSimilarly , Ġwe Ġshow Ġthe Ġplots Ġfor ĠIM Ġterm Ġin ĠFigure ĠFIG REF 15 ( c ) Ġand ĠFIG REF 15 ( d ). ĠIM Ġhas Ġonly Ġtwo Ġclasses , Ġbut Ġthey Ġare Ġvery Ġun balanced Ġin Ġtraining Ġset , Ġas Ġshown Ġin ĠTable ĠT AB REF 16 . ĠThe Ġaccuracy Ġand ĠF 1 Ġscores Ġimproved Ġfrom Ġ0 . 66 67 Ġand Ġ0 . 62 50 Ġto Ġ0 . 8 667 Ġand Ġ0 . 8 667 Ġrespectively . ĠWe Ġobserve Ġimprovements Ġin Ġthe ĠR OC Ġareas Ġfor Ġboth Ġclasses . ĠThis Ġobservation Ġfurther Ġshows Ġthat Ġour Ġmodel Ġis Ġmore Ġsensitive Ġto Ġall Ġthe Ġclass Ġsamples Ġcompared Ġto Ġthe Ġbase Ġmodel , Ġeven Ġfor Ġthe Ġterms Ġthat Ġhave Ġonly Ġa Ġfew Ġsamples Ġin Ġthe Ġtraining Ġset . ĠAgain , Ġby Ġplotting Ġthe ĠR OC Ġcurves Ġand Ġcomparing ĠA UC Ġareas , Ġwe Ġshow Ġthat Ġour Ġmodel , Ġwhich Ġapplies ĠEL Mo Ġand Ġtopic - att ention , Ġhas Ġa Ġbetter Ġrepresentation Ġability Ġunder Ġthe Ġsetting Ġof Ġfew - shot Ġlearning .