204 tokens
ABSTRACTIVE
Most texts, however, range roughly from 150 to 250 tokens.
EXTRACTIVE
FLOAT SELECTED: Figure 2: Histogram of document lengths, as measured by the number of tokens. The mean value is 204 with standard deviation of 103.
Due to the different distribution of topics in the source corpora, the 148 topics in the dataset are not represented uniformly. Three topics account for a 48.7% of the total texts and, on the other hand, a 72% of the topics are represented by 1-10 texts (Figure 1 ). This variability affects also text length. The longest text has 787 tokens and the shortest has only 16 tokens. Most texts, however, range roughly from 150 to 250 tokens. To better understand the distribution of texts in terms of word length we plot a histogram of all texts with their word length in bins of 10 (1-10 tokens, 11-20 tokens, 21-30 tokens and so on) (Figure 2 ).
1804.11346
Introduction </s> Several Ġlear ner Ġcorpor a Ġhave Ġbeen Ġcompiled Ġfor ĠEnglish , Ġsuch Ġas Ġthe ĠInternational ĠCorpus Ġof ĠLear ner ĠEnglish ĠB IB REF 0 Ġ. ĠThe Ġimportance Ġof Ġsuch Ġresources Ġhas Ġbeen Ġincreasingly Ġrecognized Ġacross Ġa Ġvariety Ġof Ġresearch Ġareas , Ġfrom ĠSecond ĠLanguage ĠAcquisition Ġto ĠNatural ĠLanguage ĠProcessing . ĠRecently , Ġwe Ġhave Ġseen Ġsubstantial Ġgrowth Ġin Ġthis Ġarea Ġand Ġnew Ġcorpor a Ġfor Ġlanguages Ġother Ġthan ĠEnglish Ġhave Ġappeared . ĠFor ĠRomance Ġlanguages , Ġthere Ġare Ġa Ġseveral Ġcorpor a Ġand Ġresources Ġfor ĠFrench , ĠSpanish ĠB IB REF 1 Ġ, Ġand ĠItalian ĠB IB REF 2 Ġ. </s> Port ug uese Ġhas Ġalso Ġreceived Ġattention Ġin Ġthe Ġcompilation Ġof Ġlear ner Ġcorpor a . ĠThere Ġare Ġtwo Ġcorpor a Ġcompiled Ġat Ġthe ĠSchool Ġof ĠArts Ġand ĠHuman ities Ġof Ġthe ĠUniversity Ġof ĠLisbon : Ġthe Ġcorpus ĠRec ol ha Ġde Ġdad os Ġde ĠA pre nd iz ag em Ġdo ĠPortug u Ãª s ĠL ÃŃ ng ua ĠE str ange ira Ġ( here after , ĠLe ir ia Ġcorpus ), Ġwith Ġ470 Ġtexts Ġand Ġ70 , 500 Ġtokens , Ġand Ġthe ĠLear ner ĠCorpus Ġof ĠPortuguese Ġas ĠSecond / Foreign ĠLanguage , ĠCOP LE 2 ĠB IB REF 3 Ġ, Ġwith Ġ1 , 05 8 Ġtexts Ġand Ġ201 , 9 21 Ġtokens . ĠThe ĠCorpus Ġde ĠProdu Ã§ Ã µ es ĠEsc rit as Ġde ĠA pre nd ent es Ġde ĠPL 2 , ĠPE AP L 2 Ġcompiled Ġat Ġthe ĠUniversity Ġof ĠCo imb ra , Ġcontains Ġ5 16 Ġtexts Ġand Ġ119 , 381 Ġtokens . ĠFinally , Ġthe ĠCorpus Ġde ĠAqu isi Ã§ Ã£o Ġde ĠL 2 , ĠCAL 2 , Ġcompiled Ġat Ġthe ĠNew ĠUniversity Ġof ĠLisbon , Ġcontains Ġ1 , 380 Ġtexts Ġand Ġ281 , 301 Ġwords , Ġand Ġit Ġincludes Ġtexts Ġproduced Ġby Ġadults Ġand Ġchildren , Ġas Ġwell Ġas Ġa Ġspoken Ġsubset . </s> The Ġaforementioned ĠPortuguese Ġlear ner Ġcorpor a Ġcontain Ġvery Ġuseful Ġdata Ġfor Ġresearch , Ġparticularly Ġfor ĠNative ĠLanguage ĠIdentification Ġ( N LI ), Ġa Ġtask Ġthat Ġhas Ġreceived Ġmuch Ġattention Ġin Ġrecent Ġyears . ĠNL I Ġis Ġthe Ġtask Ġof Ġdetermining Ġthe Ġnative Ġlanguage Ġ( L 1 ) Ġof Ġan Ġauthor Ġbased Ġon Ġtheir Ġsecond Ġlanguage Ġ( L 2 ) Ġlinguistic Ġproductions ĠB IB REF 4 Ġ. ĠNL I Ġworks Ġby Ġidentifying Ġlanguage Ġuse Ġpatterns Ġthat Ġare Ġcommon Ġto Ġgroups Ġof Ġspeakers Ġof Ġthe Ġsame Ġnative Ġlanguage . ĠThis Ġprocess Ġis Ġunderpin ned Ġby Ġthe Ġpresupp osition Ġthat Ġan Ġauthor âĢ Ļ s ĠL 1 Ġdis poses Ġthem Ġtowards Ġcertain Ġlanguage Ġproduction Ġpatterns Ġin Ġtheir ĠL 2 , Ġas Ġinfluenced Ġby Ġtheir Ġmother Ġtongue . ĠA Ġmajor Ġmotivation Ġfor ĠNL I Ġis Ġstudying Ġsecond Ġlanguage Ġacquisition . ĠNL I Ġmodels Ġcan Ġenable Ġanalysis Ġof Ġinter - L 1 Ġlinguistic Ġdifferences , Ġallowing Ġus Ġto Ġstudy Ġthe Ġlanguage Ġlearning Ġprocess Ġand Ġdevelop ĠL 1 - specific Ġped ag ogical Ġmethods Ġand Ġmaterials . </s> However , Ġthere Ġare Ġlimitations Ġto Ġusing Ġexisting ĠPortuguese Ġdata Ġfor ĠNL I . ĠAn Ġimportant Ġissue Ġis Ġthat Ġthe Ġdifferent Ġcorpor a Ġeach Ġcontain Ġdata Ġcollected Ġfrom Ġdifferent ĠL 1 Ġbackgrounds Ġin Ġvarying Ġamounts ; Ġthey Ġwould Ġneed Ġto Ġbe Ġcombined Ġto Ġhave Ġsufficient Ġdata Ġfor Ġan ĠNL I Ġstudy . ĠAnother Ġchallenge Ġconcerns Ġthe Ġannotations Ġas Ġonly Ġtwo Ġof Ġthe Ġcorpor a Ġ( PE AP L 2 Ġand ĠCOP LE 2 ) Ġare Ġlingu istically Ġannot ated , Ġand Ġthis Ġis Ġlimited Ġto ĠPOS Ġtags . ĠThe Ġdifferent Ġdata Ġformats Ġused Ġby Ġeach Ġcorpus Ġpresents Ġyet Ġanother Ġchallenge Ġto Ġtheir Ġusage . </s> In Ġthis Ġpaper Ġwe Ġpresent ĠNL I - PT , Ġa Ġdataset Ġcollected Ġfor ĠPortuguese ĠNL I . ĠThe Ġdataset Ġis Ġmade Ġfreely Ġavailable Ġfor Ġresearch Ġpurposes . ĠWith Ġthe Ġgoal Ġof Ġun ifying Ġlear ner Ġdata Ġcollected Ġfrom Ġvarious Ġsources , Ġlisted Ġin ĠSection Ġ" Collection Ġmethodology " Ġ, Ġwe Ġapplied Ġa Ġmethodology Ġwhich Ġhas Ġbeen Ġpreviously Ġused Ġfor Ġthe Ġcompilation Ġof Ġlanguage Ġvariety Ġcorpor a ĠB IB REF 5 Ġ. ĠThe Ġdata Ġwas Ġconverted Ġto Ġa Ġunified Ġdata Ġformat Ġand Ġuniformly Ġannot ated Ġat Ġdifferent Ġlinguistic Ġlevels Ġas Ġdescribed Ġin ĠSection Ġ" Pre processing Ġand Ġannotation Ġof Ġtexts " Ġ. ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , ĠNL I - PT Ġis Ġthe Ġonly ĠPortuguese Ġdataset Ġdeveloped Ġspecifically Ġfor ĠNL I , Ġthis Ġwill Ġopen Ġavenues Ġfor Ġresearch Ġin Ġthis Ġarea . </s> Related ĠWork </s> N LI Ġhas Ġattracted Ġa Ġlot Ġof Ġattention Ġin Ġrecent Ġyears . ĠDue Ġto Ġthe Ġavailability Ġof Ġsuitable Ġdata , Ġas Ġdiscussed Ġearlier , Ġthis Ġattention Ġhas Ġbeen Ġparticularly Ġfocused Ġon ĠEnglish . ĠThe Ġmost Ġnotable Ġexamples Ġare Ġthe Ġtwo Ġeditions Ġof Ġthe ĠNL I Ġshared Ġtask Ġorganized Ġin Ġ2013 ĠB IB REF 6 Ġand Ġ2017 ĠB IB REF 7 Ġ. </s> Even Ġthough Ġmost ĠNL I Ġresearch Ġhas Ġbeen Ġcarried Ġout Ġon ĠEnglish Ġdata , Ġan Ġimportant Ġresearch Ġtrend Ġin Ġrecent Ġyears Ġhas Ġbeen Ġthe Ġapplication Ġof ĠNL I Ġmethods Ġto Ġother Ġlanguages , Ġas Ġdiscussed Ġin Ġmult ilingual - n li . ĠRecent ĠNL I Ġstudies Ġon Ġlanguages Ġother Ġthan ĠEnglish Ġinclude ĠArabic ĠB IB REF 8 Ġand ĠChinese ĠB IB REF 9 Ġ, ĠB IB REF 10 Ġ. ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġno Ġstudy Ġhas Ġbeen Ġpublished Ġon ĠPortuguese Ġand Ġthe ĠNL I - PT Ġdataset Ġopens Ġnew Ġpossibilities Ġof Ġresearch Ġfor ĠPortuguese . ĠIn ĠSection Ġ" A ĠBas eline Ġfor ĠPortuguese ĠNL I " Ġwe Ġpresent Ġthe Ġfirst Ġsimple Ġbaseline Ġresults Ġfor Ġthis Ġtask . </s> Finally , Ġas ĠNL I - PT Ġcan Ġbe Ġused Ġin Ġother Ġapplications Ġbesides ĠNL I , Ġit Ġis Ġimportant Ġto Ġpoint Ġout Ġthat Ġa Ġnumber Ġof Ġstudies Ġhave Ġbeen Ġpublished Ġon Ġeducational ĠN LP Ġapplications Ġfor ĠPortuguese Ġand Ġon Ġthe Ġcompilation Ġof Ġlear ner Ġlanguage Ġresources Ġfor ĠPortuguese . ĠExamples Ġof Ġsuch Ġstudies Ġinclude Ġgram matical Ġerror Ġcorrection ĠB IB REF 11 Ġ, Ġautomated Ġessay Ġscoring ĠB IB REF 12 Ġ, Ġacademic Ġword Ġlists ĠB IB REF 13 Ġ, Ġand Ġthe Ġlear ner Ġcorpor a Ġpresented Ġin Ġthe Ġprevious Ġsection . </s> Collection Ġmethodology </s> The Ġdata Ġwas Ġcollected Ġfrom Ġthree Ġdifferent Ġlear ner Ġcorpor a Ġof ĠPortuguese : Ġ( i ) ĠCOP LE 2 ; Ġ( ii ) ĠLe ir ia Ġcorpus , Ġand Ġ( iii ) ĠPE AP L 2 Ġas Ġpresented Ġin ĠTable Ġ1 Ġ. </s> The Ġthree Ġcorpor a Ġcontain Ġwritten Ġproductions Ġfrom Ġlearners Ġof ĠPortuguese Ġwith Ġdifferent Ġproficiency Ġlevels Ġand Ġnative Ġlanguages Ġ( L 1 s ). ĠIn Ġthe Ġdataset Ġwe Ġincluded Ġall Ġthe Ġdata Ġin ĠCOP LE 2 Ġand Ġsections Ġof ĠPE AP L 2 Ġand ĠLe ir ia Ġcorpus . </s> The Ġmain Ġvariable Ġwe Ġused Ġfor Ġtext Ġselection Ġwas Ġthe Ġpresence Ġof Ġspecific ĠL 1 s . ĠSince Ġthe Ġthree Ġcorpor a Ġconsider Ġdifferent ĠL 1 s , Ġwe Ġdecided Ġto Ġuse Ġthe ĠL 1 s Ġpresent Ġin Ġthe Ġlargest Ġcorpus , ĠCOP LE 2 , Ġas Ġthe Ġreference . ĠTherefore , Ġwe Ġincluded Ġin Ġthe Ġdataset Ġtexts Ġcorresponding Ġto Ġthe Ġfollowing Ġ15 ĠL 1 s : ĠChinese , ĠEnglish , ĠSpanish , ĠGerman , ĠRussian , ĠFrench , ĠJapanese , ĠItalian , ĠDutch , ĠTet um , ĠArabic , ĠPolish , ĠKorean , ĠRomanian , Ġand ĠSwedish . ĠIt Ġwas Ġthe Ġcase Ġthat Ġsome Ġof Ġthe ĠL 1 s Ġpresent Ġin ĠCOP LE 2 Ġwere Ġnot Ġdocumented Ġin Ġthe Ġother Ġcorpor a . ĠThe Ġnumber Ġof Ġtexts Ġfrom Ġeach ĠL 1 Ġis Ġpresented Ġin ĠTable Ġ2 Ġ. </s> Con cerning Ġthe Ġcorpus Ġdesign , Ġthere Ġis Ġsome Ġvariability Ġamong Ġthe Ġsources Ġwe Ġused . ĠLe ir ia Ġcorpus Ġand ĠPE AP L 2 Ġfollowed Ġa Ġsimilar Ġapproach Ġfor Ġdata Ġcollection Ġand Ġshow Ġa Ġclose Ġdesign . ĠThey Ġconsider Ġa Ġclose Ġlist Ġof Ġtopics , Ġcalled ĠâĢ ľ stim ulus âĢ Ŀ , Ġwhich Ġbelong Ġto Ġthree Ġgeneral Ġareas : Ġ( i ) Ġthe Ġindividual ; Ġ( ii ) Ġthe Ġsociety ; Ġ( iii ) Ġthe Ġenvironment . ĠThose Ġtopics Ġare Ġpresented Ġto Ġthe Ġstudents Ġin Ġorder Ġto Ġproduce Ġa Ġwritten Ġtext . ĠAs Ġa Ġwhole , Ġtexts Ġfrom ĠPE AP L 2 Ġand ĠLe ir ia Ġrepresent Ġ36 Ġdifferent Ġstimuli Ġor Ġtopics Ġin Ġthe Ġdataset . ĠIn ĠCOP LE 2 Ġcorpus Ġthe Ġwritten Ġtexts Ġcorrespond Ġto Ġwritten Ġexercises Ġdone Ġduring ĠPortuguese Ġlessons , Ġor Ġto Ġofficial ĠPortuguese Ġproficiency Ġtests . ĠFor Ġthis Ġreason , Ġthe Ġtopics Ġconsidered Ġin ĠCOP LE 2 Ġcorpus Ġare Ġdifferent Ġfrom Ġthe Ġtopics Ġin ĠLe ir ia Ġand ĠPE AP L 2 . ĠThe Ġnumber Ġof Ġtopics Ġis Ġalso Ġlarger Ġin ĠCOP LE 2 Ġcorpus : Ġ149 Ġdifferent Ġtopics . ĠThere Ġis Ġsome Ġoverlap Ġbetween Ġthe Ġdifferent Ġtopics Ġconsidered Ġin ĠCOP LE 2 , Ġthat Ġis , Ġsome Ġtopics Ġdeal Ġwith Ġthe Ġsame Ġsubject . ĠThis Ġoverlap Ġallowed Ġus Ġto Ġreorgan ize ĠCOP LE 2 Ġtopics Ġin Ġour Ġdataset , Ġreducing Ġthem Ġto Ġ112 . </s> Due Ġto Ġthe Ġdifferent Ġdistribution Ġof Ġtopics Ġin Ġthe Ġsource Ġcorpor a , Ġthe Ġ148 Ġtopics Ġin Ġthe Ġdataset Ġare Ġnot Ġrepresented Ġuniformly . ĠThree Ġtopics Ġaccount Ġfor Ġa Ġ48 . 7 % Ġof Ġthe Ġtotal Ġtexts Ġand , Ġon Ġthe Ġother Ġhand , Ġa Ġ72 % Ġof Ġthe Ġtopics Ġare Ġrepresented Ġby Ġ1 - 10 Ġtexts Ġ( Figure Ġ1 Ġ). ĠThis Ġvariability Ġaffects Ġalso Ġtext Ġlength . ĠThe Ġlongest Ġtext Ġhas Ġ7 87 Ġtokens Ġand Ġthe Ġshortest Ġhas Ġonly Ġ16 Ġtokens . ĠMost Ġtexts , Ġhowever , Ġrange Ġroughly Ġfrom Ġ150 Ġto Ġ250 Ġtokens . ĠTo Ġbetter Ġunderstand Ġthe Ġdistribution Ġof Ġtexts Ġin Ġterms Ġof Ġword Ġlength Ġwe Ġplot Ġa Ġhist ogram Ġof Ġall Ġtexts Ġwith Ġtheir Ġword Ġlength Ġin Ġbins Ġof Ġ10 Ġ( 1 - 10 Ġtokens , Ġ11 - 20 Ġtokens , Ġ21 - 30 Ġtokens Ġand Ġso Ġon ) Ġ( Figure Ġ2 Ġ). </s> The Ġthree Ġcorpor a Ġuse Ġthe Ġproficiency Ġlevels Ġdefined Ġin Ġthe ĠCommon ĠEuropean ĠFramework Ġof ĠReference Ġfor ĠLanguages Ġ( CE FR ), Ġbut Ġthey Ġshow Ġdifferences Ġin Ġthe Ġnumber Ġof Ġlevels Ġthey Ġconsider . ĠThere Ġare Ġfive Ġproficiency Ġlevels Ġin ĠCOP LE 2 Ġand ĠPE AP L 2 : ĠA 1 , ĠA 2 , ĠB 1 , ĠB 2 , Ġand ĠC 1 . ĠBut Ġthere Ġare Ġ3 Ġlevels Ġin ĠLe ir ia Ġcorpus : ĠA , ĠB , Ġand ĠC . ĠThe Ġnumber Ġof Ġtexts Ġincluded Ġfrom Ġeach Ġproficiency Ġlevel Ġis Ġpresented Ġin ĠTable Ġ4 Ġ. </s> Pre processing Ġand Ġannotation Ġof Ġtexts </s> As Ġdemonstrated Ġearlier , Ġthese Ġlear ner Ġcorpor a Ġuse Ġdifferent Ġformats . ĠCOP LE 2 Ġis Ġmainly Ġcod ified Ġin ĠXML , Ġalthough Ġit Ġgives Ġthe Ġpossibility Ġof Ġgetting Ġthe Ġstudent Ġversion Ġof Ġthe Ġessay Ġin ĠTX T Ġformat . ĠPE AP L 2 Ġand ĠLe ir ia Ġcorpus Ġare Ġcompiled Ġin ĠTX T Ġformat . ĠIn Ġboth Ġcorpor a , Ġthe ĠTX T Ġfiles Ġcontain Ġthe Ġstudent Ġversion Ġwith Ġspecial Ġannotations Ġfrom Ġthe Ġtranscription . ĠFor Ġthe ĠNL I Ġexperiments Ġwe Ġwere Ġinterested Ġin Ġa Ġclean Ġt xt Ġversion Ġof Ġthe Ġstudents ' Ġtext , Ġtogether Ġwith Ġversions Ġannot ated Ġat Ġdifferent Ġlingu istics Ġlevels . ĠTherefore , Ġas Ġa Ġfirst Ġstep , Ġwe Ġremoved Ġall Ġthe Ġannotations Ġcorresponding Ġto Ġthe Ġtranscription Ġprocess Ġin ĠPE AP L 2 Ġand ĠLe ir ia Ġfiles . ĠAs Ġa Ġsecond Ġstep , Ġwe Ġproceeded Ġto Ġthe Ġlinguistic Ġannotation Ġof Ġthe Ġtexts Ġusing Ġdifferent ĠN LP Ġtools . </s> We Ġannot ated Ġthe Ġdataset Ġat Ġtwo Ġlevels : ĠPart Ġof ĠSpeech Ġ( POS ) Ġand Ġsyntax . ĠWe Ġperformed Ġthe Ġannotation Ġwith Ġfreely Ġavailable Ġtools Ġfor Ġthe ĠPortuguese Ġlanguage . ĠFor ĠPOS Ġwe Ġadded Ġa Ġsimple ĠPOS , Ġthat Ġis , Ġonly Ġtype Ġof Ġword , Ġand Ġa Ġfine - gr ained ĠPOS , Ġwhich Ġis Ġthe Ġtype Ġof Ġword Ġplus Ġits Ġmorph ological Ġfeatures . ĠWe Ġused Ġthe ĠLX ĠPars er ĠB IB REF 14 Ġ, Ġfor Ġthe Ġsimple ĠPOS Ġand Ġthe ĠPortuguese Ġmorph ological Ġmodule Ġof ĠFre eling ĠB IB REF 15 Ġ, Ġfor Ġdetailed ĠPOS . ĠCon cerning Ġsynt actic Ġannotations , Ġwe Ġincluded Ġconstituency Ġand Ġdependency Ġannotations . ĠFor Ġconstituency Ġparsing , Ġwe Ġused Ġthe ĠLX ĠPars er , Ġand Ġfor Ġdependency , Ġthe ĠDep Pattern Ġtool kit ĠB IB REF 16 Ġ. </s> Applications </s> N LI - PT Ġwas Ġdeveloped Ġprimarily Ġfor ĠNL I , Ġbut Ġit Ġcan Ġbe Ġused Ġfor Ġother Ġresearch Ġpurposes Ġranging Ġfrom Ġsecond Ġlanguage Ġacquisition Ġto Ġeducational ĠN LP Ġapplications . ĠHere Ġare Ġa Ġfew Ġexamples Ġof Ġapplications Ġin Ġwhich Ġthe Ġdataset Ġcan Ġbe Ġused : </s> A ĠBas eline Ġfor ĠPortuguese ĠNL I </s> To Ġdemonstrate Ġthe Ġusefulness Ġof Ġthe Ġdataset Ġwe Ġpresent Ġthe Ġfirst Ġlex ical Ġbaseline Ġfor ĠPortuguese ĠNL I Ġusing Ġa Ġsub - set Ġof ĠNL I - PT . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġno Ġstudy Ġhas Ġbeen Ġpublished Ġon ĠPortuguese ĠNL I Ġand Ġour Ġwork Ġfills Ġthis Ġgap . </s> In Ġthis Ġexperiment Ġwe Ġincluded Ġthe Ġfive ĠL 1 s Ġin ĠNL I - PT Ġwhich Ġcontain Ġthe Ġlargest Ġnumber Ġof Ġtexts Ġin Ġthis Ġsub - set Ġand Ġrun Ġa Ġsimple Ġlinear ĠS VM ĠB IB REF 21 Ġclass ifier Ġusing Ġa Ġbag Ġof Ġwords Ġmodel Ġto Ġidentify Ġthe ĠL 1 Ġof Ġeach Ġtext . ĠThe Ġlanguages Ġincluded Ġin Ġthis Ġexperiment Ġwere ĠChinese Ġ( 355 Ġtexts ), ĠEnglish Ġ( 236 Ġtexts ), ĠGerman Ġ( 214 Ġtexts ), ĠItalian Ġ( 216 Ġtexts ), Ġand ĠSpanish Ġ( 271 Ġtexts ). </s> We Ġevaluated Ġthe Ġmodel Ġusing Ġstrat ified Ġ10 - fold Ġcross - valid ation , Ġachieving Ġ70 % Ġaccuracy . ĠAn Ġimportant Ġlimitation Ġof Ġthis Ġexperiment Ġis Ġthat Ġit Ġdoes Ġnot Ġaccount Ġfor Ġtopic Ġbias , Ġan Ġimportant Ġissue Ġin ĠNL I ĠB IB REF 22 Ġ. ĠThis Ġis Ġdue Ġto Ġthe Ġfact Ġthat ĠNL I - PT Ġis Ġnot Ġbalanced Ġby Ġtopic Ġand Ġthe Ġmodel Ġcould Ġbe Ġlearning Ġtopic Ġassociations Ġinstead . ĠIn Ġfuture Ġwork Ġwe Ġwould Ġlike Ġto Ġcarry Ġout Ġusing Ġsynt actic Ġfeatures Ġsuch Ġas Ġfunction Ġwords , Ġsynt actic Ġrelations Ġand ĠPOS Ġannotation . </s> Conclusion Ġand ĠFuture ĠWork </s> This Ġpaper Ġpresented ĠNL I - PT , Ġthe Ġfirst ĠPortuguese Ġdataset Ġcompiled Ġfor ĠNL I . ĠNL I - PT Ġcontains Ġ1 , 8 68 Ġtexts Ġwritten Ġby Ġspeakers Ġof Ġ15 ĠL 1 s Ġamount ing Ġto Ġover Ġ380 , 000 Ġtokens . </s> As Ġdiscussed Ġin ĠSection Ġ" Applications " Ġ, ĠNL I - PT Ġopens Ġseveral Ġavenues Ġfor Ġfuture Ġresearch . ĠIt Ġcan Ġbe Ġused Ġfor Ġdifferent Ġresearch Ġpurposes Ġbeyond ĠNL I Ġsuch Ġas Ġgram matical Ġerror Ġcorrection Ġand ĠCALL . ĠAn Ġexperiment Ġwith Ġthe Ġtexts Ġwritten Ġby Ġthe Ġspeakers Ġof Ġfive ĠL 1 s : ĠChinese , ĠEnglish , ĠGerman , ĠItalian , Ġand ĠSpanish Ġusing Ġa Ġbag Ġof Ġwords Ġmodel Ġachieved Ġ70 % Ġaccuracy . ĠWe Ġare Ġcurrently Ġexperimenting Ġwith Ġdifferent Ġfeatures Ġtaking Ġadvantage Ġof Ġthe Ġannotation Ġavailable Ġin ĠNL I - PT Ġthus Ġreducing Ġtopic Ġbias Ġin Ġclassification . </s> In Ġfuture Ġwork Ġwe Ġwould Ġlike Ġto Ġinclude Ġmore Ġtexts Ġin Ġthe Ġdataset Ġfollowing Ġthe Ġsame Ġmethodology Ġand Ġannotation . </s> Acknowled gement </s> We Ġwant Ġto Ġthank Ġthe Ġresearch Ġteams Ġthat Ġhave Ġmade Ġavailable Ġthe Ġdata Ġwe Ġused Ġin Ġthis Ġwork : ĠCent ro Ġde ĠEst udos Ġde ĠL ingu ÃŃ st ica ĠGeral Ġe ĠA pl ic ada Ġat ĠUnivers id ade Ġde ĠCo imb ra Ġ( s pecially ĠCrist ina ĠMart ins ) Ġand ĠCent ro Ġde ĠL ingu ÃŃ st ica Ġda ĠUnivers id ade Ġde ĠLis boa Ġ( particularly ĠAm Ã¡ lia ĠMend es ). </s> This Ġwork Ġwas Ġpartially Ġsupported Ġby ĠFund a Ã§ Ã£o Ġpara Ġa ĠCi Ãª nc ia Ġe Ġa ĠTec n olog ia Ġ( post doctoral Ġresearch Ġgrant ĠS FR H / B PD / 10 99 14 / 2015 ).
Introduction Several learner corpora have been compiled for English, such as the International Corpus of Learner English BIBREF0 . The importance of such resources has been increasingly recognized across a variety of research areas, from Second Language Acquisition to Natural Language Processing. Recently, we have seen substantial growth in this area and new corpora for languages other than English have appeared. For Romance languages, there are a several corpora and resources for French, Spanish BIBREF1 , and Italian BIBREF2 . Portuguese has also received attention in the compilation of learner corpora. There are two corpora compiled at the School of Arts and Humanities of the University of Lisbon: the corpus Recolha de dados de Aprendizagem do Português Língua Estrangeira (hereafter, Leiria corpus), with 470 texts and 70,500 tokens, and the Learner Corpus of Portuguese as Second/Foreign Language, COPLE2 BIBREF3 , with 1,058 texts and 201,921 tokens. The Corpus de Produções Escritas de Aprendentes de PL2, PEAPL2 compiled at the University of Coimbra, contains 516 texts and 119,381 tokens. Finally, the Corpus de Aquisição de L2, CAL2, compiled at the New University of Lisbon, contains 1,380 texts and 281,301 words, and it includes texts produced by adults and children, as well as a spoken subset. The aforementioned Portuguese learner corpora contain very useful data for research, particularly for Native Language Identification (NLI), a task that has received much attention in recent years. NLI is the task of determining the native language (L1) of an author based on their second language (L2) linguistic productions BIBREF4 . NLI works by identifying language use patterns that are common to groups of speakers of the same native language. This process is underpinned by the presupposition that an author’s L1 disposes them towards certain language production patterns in their L2, as influenced by their mother tongue. A major motivation for NLI is studying second language acquisition. NLI models can enable analysis of inter-L1 linguistic differences, allowing us to study the language learning process and develop L1-specific pedagogical methods and materials. However, there are limitations to using existing Portuguese data for NLI. An important issue is that the different corpora each contain data collected from different L1 backgrounds in varying amounts; they would need to be combined to have sufficient data for an NLI study. Another challenge concerns the annotations as only two of the corpora (PEAPL2 and COPLE2) are linguistically annotated, and this is limited to POS tags. The different data formats used by each corpus presents yet another challenge to their usage. In this paper we present NLI-PT, a dataset collected for Portuguese NLI. The dataset is made freely available for research purposes. With the goal of unifying learner data collected from various sources, listed in Section "Collection methodology" , we applied a methodology which has been previously used for the compilation of language variety corpora BIBREF5 . The data was converted to a unified data format and uniformly annotated at different linguistic levels as described in Section "Preprocessing and annotation of texts" . To the best of our knowledge, NLI-PT is the only Portuguese dataset developed specifically for NLI, this will open avenues for research in this area. Related Work NLI has attracted a lot of attention in recent years. Due to the availability of suitable data, as discussed earlier, this attention has been particularly focused on English. The most notable examples are the two editions of the NLI shared task organized in 2013 BIBREF6 and 2017 BIBREF7 . Even though most NLI research has been carried out on English data, an important research trend in recent years has been the application of NLI methods to other languages, as discussed in multilingual-nli. Recent NLI studies on languages other than English include Arabic BIBREF8 and Chinese BIBREF9 , BIBREF10 . To the best of our knowledge, no study has been published on Portuguese and the NLI-PT dataset opens new possibilities of research for Portuguese. In Section "A Baseline for Portuguese NLI" we present the first simple baseline results for this task. Finally, as NLI-PT can be used in other applications besides NLI, it is important to point out that a number of studies have been published on educational NLP applications for Portuguese and on the compilation of learner language resources for Portuguese. Examples of such studies include grammatical error correction BIBREF11 , automated essay scoring BIBREF12 , academic word lists BIBREF13 , and the learner corpora presented in the previous section. Collection methodology The data was collected from three different learner corpora of Portuguese: (i) COPLE2; (ii) Leiria corpus, and (iii) PEAPL2 as presented in Table 1 . The three corpora contain written productions from learners of Portuguese with different proficiency levels and native languages (L1s). In the dataset we included all the data in COPLE2 and sections of PEAPL2 and Leiria corpus. The main variable we used for text selection was the presence of specific L1s. Since the three corpora consider different L1s, we decided to use the L1s present in the largest corpus, COPLE2, as the reference. Therefore, we included in the dataset texts corresponding to the following 15 L1s: Chinese, English, Spanish, German, Russian, French, Japanese, Italian, Dutch, Tetum, Arabic, Polish, Korean, Romanian, and Swedish. It was the case that some of the L1s present in COPLE2 were not documented in the other corpora. The number of texts from each L1 is presented in Table 2 . Concerning the corpus design, there is some variability among the sources we used. Leiria corpus and PEAPL2 followed a similar approach for data collection and show a close design. They consider a close list of topics, called “stimulus”, which belong to three general areas: (i) the individual; (ii) the society; (iii) the environment. Those topics are presented to the students in order to produce a written text. As a whole, texts from PEAPL2 and Leiria represent 36 different stimuli or topics in the dataset. In COPLE2 corpus the written texts correspond to written exercises done during Portuguese lessons, or to official Portuguese proficiency tests. For this reason, the topics considered in COPLE2 corpus are different from the topics in Leiria and PEAPL2. The number of topics is also larger in COPLE2 corpus: 149 different topics. There is some overlap between the different topics considered in COPLE2, that is, some topics deal with the same subject. This overlap allowed us to reorganize COPLE2 topics in our dataset, reducing them to 112. Due to the different distribution of topics in the source corpora, the 148 topics in the dataset are not represented uniformly. Three topics account for a 48.7% of the total texts and, on the other hand, a 72% of the topics are represented by 1-10 texts (Figure 1 ). This variability affects also text length. The longest text has 787 tokens and the shortest has only 16 tokens. Most texts, however, range roughly from 150 to 250 tokens. To better understand the distribution of texts in terms of word length we plot a histogram of all texts with their word length in bins of 10 (1-10 tokens, 11-20 tokens, 21-30 tokens and so on) (Figure 2 ). The three corpora use the proficiency levels defined in the Common European Framework of Reference for Languages (CEFR), but they show differences in the number of levels they consider. There are five proficiency levels in COPLE2 and PEAPL2: A1, A2, B1, B2, and C1. But there are 3 levels in Leiria corpus: A, B, and C. The number of texts included from each proficiency level is presented in Table 4 . Preprocessing and annotation of texts As demonstrated earlier, these learner corpora use different formats. COPLE2 is mainly codified in XML, although it gives the possibility of getting the student version of the essay in TXT format. PEAPL2 and Leiria corpus are compiled in TXT format. In both corpora, the TXT files contain the student version with special annotations from the transcription. For the NLI experiments we were interested in a clean txt version of the students' text, together with versions annotated at different linguistics levels. Therefore, as a first step, we removed all the annotations corresponding to the transcription process in PEAPL2 and Leiria files. As a second step, we proceeded to the linguistic annotation of the texts using different NLP tools. We annotated the dataset at two levels: Part of Speech (POS) and syntax. We performed the annotation with freely available tools for the Portuguese language. For POS we added a simple POS, that is, only type of word, and a fine-grained POS, which is the type of word plus its morphological features. We used the LX Parser BIBREF14 , for the simple POS and the Portuguese morphological module of Freeling BIBREF15 , for detailed POS. Concerning syntactic annotations, we included constituency and dependency annotations. For constituency parsing, we used the LX Parser, and for dependency, the DepPattern toolkit BIBREF16 . Applications NLI-PT was developed primarily for NLI, but it can be used for other research purposes ranging from second language acquisition to educational NLP applications. Here are a few examples of applications in which the dataset can be used: A Baseline for Portuguese NLI To demonstrate the usefulness of the dataset we present the first lexical baseline for Portuguese NLI using a sub-set of NLI-PT. To the best of our knowledge, no study has been published on Portuguese NLI and our work fills this gap. In this experiment we included the five L1s in NLI-PT which contain the largest number of texts in this sub-set and run a simple linear SVM BIBREF21 classifier using a bag of words model to identify the L1 of each text. The languages included in this experiment were Chinese (355 texts), English (236 texts), German (214 texts), Italian (216 texts), and Spanish (271 texts). We evaluated the model using stratified 10-fold cross-validation, achieving 70% accuracy. An important limitation of this experiment is that it does not account for topic bias, an important issue in NLI BIBREF22 . This is due to the fact that NLI-PT is not balanced by topic and the model could be learning topic associations instead. In future work we would like to carry out using syntactic features such as function words, syntactic relations and POS annotation. Conclusion and Future Work This paper presented NLI-PT, the first Portuguese dataset compiled for NLI. NLI-PT contains 1,868 texts written by speakers of 15 L1s amounting to over 380,000 tokens. As discussed in Section "Applications" , NLI-PT opens several avenues for future research. It can be used for different research purposes beyond NLI such as grammatical error correction and CALL. An experiment with the texts written by the speakers of five L1s: Chinese, English, German, Italian, and Spanish using a bag of words model achieved 70% accuracy. We are currently experimenting with different features taking advantage of the annotation available in NLI-PT thus reducing topic bias in classification. In future work we would like to include more texts in the dataset following the same methodology and annotation. Acknowledgement We want to thank the research teams that have made available the data we used in this work: Centro de Estudos de Linguística Geral e Aplicada at Universidade de Coimbra (specially Cristina Martins) and Centro de Linguística da Universidade de Lisboa (particularly Amália Mendes). This work was partially supported by Fundação para a Ciência e a Tecnologia (postdoctoral research grant SFRH/BPD/109914/2015).
How long are the essays on average?
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How Ġlong Ġare Ġthe Ġessays Ġon Ġaverage ?
204 Ġtokens
<s> How Ġlong Ġare Ġthe Ġessays Ġon Ġaverage ? </s> Introduction </s> Several Ġlear ner Ġcorpor a Ġhave Ġbeen Ġcompiled Ġfor ĠEnglish , Ġsuch Ġas Ġthe ĠInternational ĠCorpus Ġof ĠLear ner ĠEnglish ĠB IB REF 0 Ġ. ĠThe Ġimportance Ġof Ġsuch Ġresources Ġhas Ġbeen Ġincreasingly Ġrecognized Ġacross Ġa Ġvariety Ġof Ġresearch Ġareas , Ġfrom ĠSecond ĠLanguage ĠAcquisition Ġto ĠNatural ĠLanguage ĠProcessing . ĠRecently , Ġwe Ġhave Ġseen Ġsubstantial Ġgrowth Ġin Ġthis Ġarea Ġand Ġnew Ġcorpor a Ġfor Ġlanguages Ġother Ġthan ĠEnglish Ġhave Ġappeared . ĠFor ĠRomance Ġlanguages , Ġthere Ġare Ġa Ġseveral Ġcorpor a Ġand Ġresources Ġfor ĠFrench , ĠSpanish ĠB IB REF 1 Ġ, Ġand ĠItalian ĠB IB REF 2 Ġ. </s> Port ug uese Ġhas Ġalso Ġreceived Ġattention Ġin Ġthe Ġcompilation Ġof Ġlear ner Ġcorpor a . ĠThere Ġare Ġtwo Ġcorpor a Ġcompiled Ġat Ġthe ĠSchool Ġof ĠArts Ġand ĠHuman ities Ġof Ġthe ĠUniversity Ġof ĠLisbon : Ġthe Ġcorpus ĠRec ol ha Ġde Ġdad os Ġde ĠA pre nd iz ag em Ġdo ĠPortug u Ãª s ĠL ÃŃ ng ua ĠE str ange ira Ġ( here after , ĠLe ir ia Ġcorpus ), Ġwith Ġ470 Ġtexts Ġand Ġ70 , 500 Ġtokens , Ġand Ġthe ĠLear ner ĠCorpus Ġof ĠPortuguese Ġas ĠSecond / Foreign ĠLanguage , ĠCOP LE 2 ĠB IB REF 3 Ġ, Ġwith Ġ1 , 05 8 Ġtexts Ġand Ġ201 , 9 21 Ġtokens . ĠThe ĠCorpus Ġde ĠProdu Ã§ Ã µ es ĠEsc rit as Ġde ĠA pre nd ent es Ġde ĠPL 2 , ĠPE AP L 2 Ġcompiled Ġat Ġthe ĠUniversity Ġof ĠCo imb ra , Ġcontains Ġ5 16 Ġtexts Ġand Ġ119 , 381 Ġtokens . ĠFinally , Ġthe ĠCorpus Ġde ĠAqu isi Ã§ Ã£o Ġde ĠL 2 , ĠCAL 2 , Ġcompiled Ġat Ġthe ĠNew ĠUniversity Ġof ĠLisbon , Ġcontains Ġ1 , 380 Ġtexts Ġand Ġ281 , 301 Ġwords , Ġand Ġit Ġincludes Ġtexts Ġproduced Ġby Ġadults Ġand Ġchildren , Ġas Ġwell Ġas Ġa Ġspoken Ġsubset . </s> The Ġaforementioned ĠPortuguese Ġlear ner Ġcorpor a Ġcontain Ġvery Ġuseful Ġdata Ġfor Ġresearch , Ġparticularly Ġfor ĠNative ĠLanguage ĠIdentification Ġ( N LI ), Ġa Ġtask Ġthat Ġhas Ġreceived Ġmuch Ġattention Ġin Ġrecent Ġyears . ĠNL I Ġis Ġthe Ġtask Ġof Ġdetermining Ġthe Ġnative Ġlanguage Ġ( L 1 ) Ġof Ġan Ġauthor Ġbased Ġon Ġtheir Ġsecond Ġlanguage Ġ( L 2 ) Ġlinguistic Ġproductions ĠB IB REF 4 Ġ. ĠNL I Ġworks Ġby Ġidentifying Ġlanguage Ġuse Ġpatterns Ġthat Ġare Ġcommon Ġto Ġgroups Ġof Ġspeakers Ġof Ġthe Ġsame Ġnative Ġlanguage . ĠThis Ġprocess Ġis Ġunderpin ned Ġby Ġthe Ġpresupp osition Ġthat Ġan Ġauthor âĢ Ļ s ĠL 1 Ġdis poses Ġthem Ġtowards Ġcertain Ġlanguage Ġproduction Ġpatterns Ġin Ġtheir ĠL 2 , Ġas Ġinfluenced Ġby Ġtheir Ġmother Ġtongue . ĠA Ġmajor Ġmotivation Ġfor ĠNL I Ġis Ġstudying Ġsecond Ġlanguage Ġacquisition . ĠNL I Ġmodels Ġcan Ġenable Ġanalysis Ġof Ġinter - L 1 Ġlinguistic Ġdifferences , Ġallowing Ġus Ġto Ġstudy Ġthe Ġlanguage Ġlearning Ġprocess Ġand Ġdevelop ĠL 1 - specific Ġped ag ogical Ġmethods Ġand Ġmaterials . </s> However , Ġthere Ġare Ġlimitations Ġto Ġusing Ġexisting ĠPortuguese Ġdata Ġfor ĠNL I . ĠAn Ġimportant Ġissue Ġis Ġthat Ġthe Ġdifferent Ġcorpor a Ġeach Ġcontain Ġdata Ġcollected Ġfrom Ġdifferent ĠL 1 Ġbackgrounds Ġin Ġvarying Ġamounts ; Ġthey Ġwould Ġneed Ġto Ġbe Ġcombined Ġto Ġhave Ġsufficient Ġdata Ġfor Ġan ĠNL I Ġstudy . ĠAnother Ġchallenge Ġconcerns Ġthe Ġannotations Ġas Ġonly Ġtwo Ġof Ġthe Ġcorpor a Ġ( PE AP L 2 Ġand ĠCOP LE 2 ) Ġare Ġlingu istically Ġannot ated , Ġand Ġthis Ġis Ġlimited Ġto ĠPOS Ġtags . ĠThe Ġdifferent Ġdata Ġformats Ġused Ġby Ġeach Ġcorpus Ġpresents Ġyet Ġanother Ġchallenge Ġto Ġtheir Ġusage . </s> In Ġthis Ġpaper Ġwe Ġpresent ĠNL I - PT , Ġa Ġdataset Ġcollected Ġfor ĠPortuguese ĠNL I . ĠThe Ġdataset Ġis Ġmade Ġfreely Ġavailable Ġfor Ġresearch Ġpurposes . ĠWith Ġthe Ġgoal Ġof Ġun ifying Ġlear ner Ġdata Ġcollected Ġfrom Ġvarious Ġsources , Ġlisted Ġin ĠSection Ġ" Collection Ġmethodology " Ġ, Ġwe Ġapplied Ġa Ġmethodology Ġwhich Ġhas Ġbeen Ġpreviously Ġused Ġfor Ġthe Ġcompilation Ġof Ġlanguage Ġvariety Ġcorpor a ĠB IB REF 5 Ġ. ĠThe Ġdata Ġwas Ġconverted Ġto Ġa Ġunified Ġdata Ġformat Ġand Ġuniformly Ġannot ated Ġat Ġdifferent Ġlinguistic Ġlevels Ġas Ġdescribed Ġin ĠSection Ġ" Pre processing Ġand Ġannotation Ġof Ġtexts " Ġ. ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , ĠNL I - PT Ġis Ġthe Ġonly ĠPortuguese Ġdataset Ġdeveloped Ġspecifically Ġfor ĠNL I , Ġthis Ġwill Ġopen Ġavenues Ġfor Ġresearch Ġin Ġthis Ġarea . </s> Related ĠWork </s> N LI Ġhas Ġattracted Ġa Ġlot Ġof Ġattention Ġin Ġrecent Ġyears . ĠDue Ġto Ġthe Ġavailability Ġof Ġsuitable Ġdata , Ġas Ġdiscussed Ġearlier , Ġthis Ġattention Ġhas Ġbeen Ġparticularly Ġfocused Ġon ĠEnglish . ĠThe Ġmost Ġnotable Ġexamples Ġare Ġthe Ġtwo Ġeditions Ġof Ġthe ĠNL I Ġshared Ġtask Ġorganized Ġin Ġ2013 ĠB IB REF 6 Ġand Ġ2017 ĠB IB REF 7 Ġ. </s> Even Ġthough Ġmost ĠNL I Ġresearch Ġhas Ġbeen Ġcarried Ġout Ġon ĠEnglish Ġdata , Ġan Ġimportant Ġresearch Ġtrend Ġin Ġrecent Ġyears Ġhas Ġbeen Ġthe Ġapplication Ġof ĠNL I Ġmethods Ġto Ġother Ġlanguages , Ġas Ġdiscussed Ġin Ġmult ilingual - n li . ĠRecent ĠNL I Ġstudies Ġon Ġlanguages Ġother Ġthan ĠEnglish Ġinclude ĠArabic ĠB IB REF 8 Ġand ĠChinese ĠB IB REF 9 Ġ, ĠB IB REF 10 Ġ. ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġno Ġstudy Ġhas Ġbeen Ġpublished Ġon ĠPortuguese Ġand Ġthe ĠNL I - PT Ġdataset Ġopens Ġnew Ġpossibilities Ġof Ġresearch Ġfor ĠPortuguese . ĠIn ĠSection Ġ" A ĠBas eline Ġfor ĠPortuguese ĠNL I " Ġwe Ġpresent Ġthe Ġfirst Ġsimple Ġbaseline Ġresults Ġfor Ġthis Ġtask . </s> Finally , Ġas ĠNL I - PT Ġcan Ġbe Ġused Ġin Ġother Ġapplications Ġbesides ĠNL I , Ġit Ġis Ġimportant Ġto Ġpoint Ġout Ġthat Ġa Ġnumber Ġof Ġstudies Ġhave Ġbeen Ġpublished Ġon Ġeducational ĠN LP Ġapplications Ġfor ĠPortuguese Ġand Ġon Ġthe Ġcompilation Ġof Ġlear ner Ġlanguage Ġresources Ġfor ĠPortuguese . ĠExamples Ġof Ġsuch Ġstudies Ġinclude Ġgram matical Ġerror Ġcorrection ĠB IB REF 11 Ġ, Ġautomated Ġessay Ġscoring ĠB IB REF 12 Ġ, Ġacademic Ġword Ġlists ĠB IB REF 13 Ġ, Ġand Ġthe Ġlear ner Ġcorpor a Ġpresented Ġin Ġthe Ġprevious Ġsection . </s> Collection Ġmethodology </s> The Ġdata Ġwas Ġcollected Ġfrom Ġthree Ġdifferent Ġlear ner Ġcorpor a Ġof ĠPortuguese : Ġ( i ) ĠCOP LE 2 ; Ġ( ii ) ĠLe ir ia Ġcorpus , Ġand Ġ( iii ) ĠPE AP L 2 Ġas Ġpresented Ġin ĠTable Ġ1 Ġ. </s> The Ġthree Ġcorpor a Ġcontain Ġwritten Ġproductions Ġfrom Ġlearners Ġof ĠPortuguese Ġwith Ġdifferent Ġproficiency Ġlevels Ġand Ġnative Ġlanguages Ġ( L 1 s ). ĠIn Ġthe Ġdataset Ġwe Ġincluded Ġall Ġthe Ġdata Ġin ĠCOP LE 2 Ġand Ġsections Ġof ĠPE AP L 2 Ġand ĠLe ir ia Ġcorpus . </s> The Ġmain Ġvariable Ġwe Ġused Ġfor Ġtext Ġselection Ġwas Ġthe Ġpresence Ġof Ġspecific ĠL 1 s . ĠSince Ġthe Ġthree Ġcorpor a Ġconsider Ġdifferent ĠL 1 s , Ġwe Ġdecided Ġto Ġuse Ġthe ĠL 1 s Ġpresent Ġin Ġthe Ġlargest Ġcorpus , ĠCOP LE 2 , Ġas Ġthe Ġreference . ĠTherefore , Ġwe Ġincluded Ġin Ġthe Ġdataset Ġtexts Ġcorresponding Ġto Ġthe Ġfollowing Ġ15 ĠL 1 s : ĠChinese , ĠEnglish , ĠSpanish , ĠGerman , ĠRussian , ĠFrench , ĠJapanese , ĠItalian , ĠDutch , ĠTet um , ĠArabic , ĠPolish , ĠKorean , ĠRomanian , Ġand ĠSwedish . ĠIt Ġwas Ġthe Ġcase Ġthat Ġsome Ġof Ġthe ĠL 1 s Ġpresent Ġin ĠCOP LE 2 Ġwere Ġnot Ġdocumented Ġin Ġthe Ġother Ġcorpor a . ĠThe Ġnumber Ġof Ġtexts Ġfrom Ġeach ĠL 1 Ġis Ġpresented Ġin ĠTable Ġ2 Ġ. </s> Con cerning Ġthe Ġcorpus Ġdesign , Ġthere Ġis Ġsome Ġvariability Ġamong Ġthe Ġsources Ġwe Ġused . ĠLe ir ia Ġcorpus Ġand ĠPE AP L 2 Ġfollowed Ġa Ġsimilar Ġapproach Ġfor Ġdata Ġcollection Ġand Ġshow Ġa Ġclose Ġdesign . ĠThey Ġconsider Ġa Ġclose Ġlist Ġof Ġtopics , Ġcalled ĠâĢ ľ stim ulus âĢ Ŀ , Ġwhich Ġbelong Ġto Ġthree Ġgeneral Ġareas : Ġ( i ) Ġthe Ġindividual ; Ġ( ii ) Ġthe Ġsociety ; Ġ( iii ) Ġthe Ġenvironment . ĠThose Ġtopics Ġare Ġpresented Ġto Ġthe Ġstudents Ġin Ġorder Ġto Ġproduce Ġa Ġwritten Ġtext . ĠAs Ġa Ġwhole , Ġtexts Ġfrom ĠPE AP L 2 Ġand ĠLe ir ia Ġrepresent Ġ36 Ġdifferent Ġstimuli Ġor Ġtopics Ġin Ġthe Ġdataset . ĠIn ĠCOP LE 2 Ġcorpus Ġthe Ġwritten Ġtexts Ġcorrespond Ġto Ġwritten Ġexercises Ġdone Ġduring ĠPortuguese Ġlessons , Ġor Ġto Ġofficial ĠPortuguese Ġproficiency Ġtests . ĠFor Ġthis Ġreason , Ġthe Ġtopics Ġconsidered Ġin ĠCOP LE 2 Ġcorpus Ġare Ġdifferent Ġfrom Ġthe Ġtopics Ġin ĠLe ir ia Ġand ĠPE AP L 2 . ĠThe Ġnumber Ġof Ġtopics Ġis Ġalso Ġlarger Ġin ĠCOP LE 2 Ġcorpus : Ġ149 Ġdifferent Ġtopics . ĠThere Ġis Ġsome Ġoverlap Ġbetween Ġthe Ġdifferent Ġtopics Ġconsidered Ġin ĠCOP LE 2 , Ġthat Ġis , Ġsome Ġtopics Ġdeal Ġwith Ġthe Ġsame Ġsubject . ĠThis Ġoverlap Ġallowed Ġus Ġto Ġreorgan ize ĠCOP LE 2 Ġtopics Ġin Ġour Ġdataset , Ġreducing Ġthem Ġto Ġ112 . </s> Due Ġto Ġthe Ġdifferent Ġdistribution Ġof Ġtopics Ġin Ġthe Ġsource Ġcorpor a , Ġthe Ġ148 Ġtopics Ġin Ġthe Ġdataset Ġare Ġnot Ġrepresented Ġuniformly . ĠThree Ġtopics Ġaccount Ġfor Ġa Ġ48 . 7 % Ġof Ġthe Ġtotal Ġtexts Ġand , Ġon Ġthe Ġother Ġhand , Ġa Ġ72 % Ġof Ġthe Ġtopics Ġare Ġrepresented Ġby Ġ1 - 10 Ġtexts Ġ( Figure Ġ1 Ġ). ĠThis Ġvariability Ġaffects Ġalso Ġtext Ġlength . ĠThe Ġlongest Ġtext Ġhas Ġ7 87 Ġtokens Ġand Ġthe Ġshortest Ġhas Ġonly Ġ16 Ġtokens . ĠMost Ġtexts , Ġhowever , Ġrange Ġroughly Ġfrom Ġ150 Ġto Ġ250 Ġtokens . ĠTo Ġbetter Ġunderstand Ġthe Ġdistribution Ġof Ġtexts Ġin Ġterms Ġof Ġword Ġlength Ġwe Ġplot Ġa Ġhist ogram Ġof Ġall Ġtexts Ġwith Ġtheir Ġword Ġlength Ġin Ġbins Ġof Ġ10 Ġ( 1 - 10 Ġtokens , Ġ11 - 20 Ġtokens , Ġ21 - 30 Ġtokens Ġand Ġso Ġon ) Ġ( Figure Ġ2 Ġ). </s> The Ġthree Ġcorpor a Ġuse Ġthe Ġproficiency Ġlevels Ġdefined Ġin Ġthe ĠCommon ĠEuropean ĠFramework Ġof ĠReference Ġfor ĠLanguages Ġ( CE FR ), Ġbut Ġthey Ġshow Ġdifferences Ġin Ġthe Ġnumber Ġof Ġlevels Ġthey Ġconsider . ĠThere Ġare Ġfive Ġproficiency Ġlevels Ġin ĠCOP LE 2 Ġand ĠPE AP L 2 : ĠA 1 , ĠA 2 , ĠB 1 , ĠB 2 , Ġand ĠC 1 . ĠBut Ġthere Ġare Ġ3 Ġlevels Ġin ĠLe ir ia Ġcorpus : ĠA , ĠB , Ġand ĠC . ĠThe Ġnumber Ġof Ġtexts Ġincluded Ġfrom Ġeach Ġproficiency Ġlevel Ġis Ġpresented Ġin ĠTable Ġ4 Ġ. </s> Pre processing Ġand Ġannotation Ġof Ġtexts </s> As Ġdemonstrated Ġearlier , Ġthese Ġlear ner Ġcorpor a Ġuse Ġdifferent Ġformats . ĠCOP LE 2 Ġis Ġmainly Ġcod ified Ġin ĠXML , Ġalthough Ġit Ġgives Ġthe Ġpossibility Ġof Ġgetting Ġthe Ġstudent Ġversion Ġof Ġthe Ġessay Ġin ĠTX T Ġformat . ĠPE AP L 2 Ġand ĠLe ir ia Ġcorpus Ġare Ġcompiled Ġin ĠTX T Ġformat . ĠIn Ġboth Ġcorpor a , Ġthe ĠTX T Ġfiles Ġcontain Ġthe Ġstudent Ġversion Ġwith Ġspecial Ġannotations Ġfrom Ġthe Ġtranscription . ĠFor Ġthe ĠNL I Ġexperiments Ġwe Ġwere Ġinterested Ġin Ġa Ġclean Ġt xt Ġversion Ġof Ġthe Ġstudents ' Ġtext , Ġtogether Ġwith Ġversions Ġannot ated Ġat Ġdifferent Ġlingu istics Ġlevels . ĠTherefore , Ġas Ġa Ġfirst Ġstep , Ġwe Ġremoved Ġall Ġthe Ġannotations Ġcorresponding Ġto Ġthe Ġtranscription Ġprocess Ġin ĠPE AP L 2 Ġand ĠLe ir ia Ġfiles . ĠAs Ġa Ġsecond Ġstep , Ġwe Ġproceeded Ġto Ġthe Ġlinguistic Ġannotation Ġof Ġthe Ġtexts Ġusing Ġdifferent ĠN LP Ġtools . </s> We Ġannot ated Ġthe Ġdataset Ġat Ġtwo Ġlevels : ĠPart Ġof ĠSpeech Ġ( POS ) Ġand Ġsyntax . ĠWe Ġperformed Ġthe Ġannotation Ġwith Ġfreely Ġavailable Ġtools Ġfor Ġthe ĠPortuguese Ġlanguage . ĠFor ĠPOS Ġwe Ġadded Ġa Ġsimple ĠPOS , Ġthat Ġis , Ġonly Ġtype Ġof Ġword , Ġand Ġa Ġfine - gr ained ĠPOS , Ġwhich Ġis Ġthe Ġtype Ġof Ġword Ġplus Ġits Ġmorph ological Ġfeatures . ĠWe Ġused Ġthe ĠLX ĠPars er ĠB IB REF 14 Ġ, Ġfor Ġthe Ġsimple ĠPOS Ġand Ġthe ĠPortuguese Ġmorph ological Ġmodule Ġof ĠFre eling ĠB IB REF 15 Ġ, Ġfor Ġdetailed ĠPOS . ĠCon cerning Ġsynt actic Ġannotations , Ġwe Ġincluded Ġconstituency Ġand Ġdependency Ġannotations . ĠFor Ġconstituency Ġparsing , Ġwe Ġused Ġthe ĠLX ĠPars er , Ġand Ġfor Ġdependency , Ġthe ĠDep Pattern Ġtool kit ĠB IB REF 16 Ġ. </s> Applications </s> N LI - PT Ġwas Ġdeveloped Ġprimarily Ġfor ĠNL I , Ġbut Ġit Ġcan Ġbe Ġused Ġfor Ġother Ġresearch Ġpurposes Ġranging Ġfrom Ġsecond Ġlanguage Ġacquisition Ġto Ġeducational ĠN LP Ġapplications . ĠHere Ġare Ġa Ġfew Ġexamples Ġof Ġapplications Ġin Ġwhich Ġthe Ġdataset Ġcan Ġbe Ġused : </s> A ĠBas eline Ġfor ĠPortuguese ĠNL I </s> To Ġdemonstrate Ġthe Ġusefulness Ġof Ġthe Ġdataset Ġwe Ġpresent Ġthe Ġfirst Ġlex ical Ġbaseline Ġfor ĠPortuguese ĠNL I Ġusing Ġa Ġsub - set Ġof ĠNL I - PT . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , Ġno Ġstudy Ġhas Ġbeen Ġpublished Ġon ĠPortuguese ĠNL I Ġand Ġour Ġwork Ġfills Ġthis Ġgap . </s> In Ġthis Ġexperiment Ġwe Ġincluded Ġthe Ġfive ĠL 1 s Ġin ĠNL I - PT Ġwhich Ġcontain Ġthe Ġlargest Ġnumber Ġof Ġtexts Ġin Ġthis Ġsub - set Ġand Ġrun Ġa Ġsimple Ġlinear ĠS VM ĠB IB REF 21 Ġclass ifier Ġusing Ġa Ġbag Ġof Ġwords Ġmodel Ġto Ġidentify Ġthe ĠL 1 Ġof Ġeach Ġtext . ĠThe Ġlanguages Ġincluded Ġin Ġthis Ġexperiment Ġwere ĠChinese Ġ( 355 Ġtexts ), ĠEnglish Ġ( 236 Ġtexts ), ĠGerman Ġ( 214 Ġtexts ), ĠItalian Ġ( 216 Ġtexts ), Ġand ĠSpanish Ġ( 271 Ġtexts ). </s> We Ġevaluated Ġthe Ġmodel Ġusing Ġstrat ified Ġ10 - fold Ġcross - valid ation , Ġachieving Ġ70 % Ġaccuracy . ĠAn Ġimportant Ġlimitation Ġof Ġthis Ġexperiment Ġis Ġthat Ġit Ġdoes Ġnot Ġaccount Ġfor Ġtopic Ġbias , Ġan Ġimportant Ġissue Ġin ĠNL I ĠB IB REF 22 Ġ. ĠThis Ġis Ġdue Ġto Ġthe Ġfact Ġthat ĠNL I - PT Ġis Ġnot Ġbalanced Ġby Ġtopic Ġand Ġthe Ġmodel Ġcould Ġbe Ġlearning Ġtopic Ġassociations Ġinstead . ĠIn Ġfuture Ġwork Ġwe Ġwould Ġlike Ġto Ġcarry Ġout Ġusing Ġsynt actic Ġfeatures Ġsuch Ġas Ġfunction Ġwords , Ġsynt actic Ġrelations Ġand ĠPOS Ġannotation . </s> Conclusion Ġand ĠFuture ĠWork </s> This Ġpaper Ġpresented ĠNL I - PT , Ġthe Ġfirst ĠPortuguese Ġdataset Ġcompiled Ġfor ĠNL I . ĠNL I - PT Ġcontains Ġ1 , 8 68 Ġtexts Ġwritten Ġby Ġspeakers Ġof Ġ15 ĠL 1 s Ġamount ing Ġto Ġover Ġ380 , 000 Ġtokens . </s> As Ġdiscussed Ġin ĠSection Ġ" Applications " Ġ, ĠNL I - PT Ġopens Ġseveral Ġavenues Ġfor Ġfuture Ġresearch . ĠIt Ġcan Ġbe Ġused Ġfor Ġdifferent Ġresearch Ġpurposes Ġbeyond ĠNL I Ġsuch Ġas Ġgram matical Ġerror Ġcorrection Ġand ĠCALL . ĠAn Ġexperiment Ġwith Ġthe Ġtexts Ġwritten Ġby Ġthe Ġspeakers Ġof Ġfive ĠL 1 s : ĠChinese , ĠEnglish , ĠGerman , ĠItalian , Ġand ĠSpanish Ġusing Ġa Ġbag Ġof Ġwords Ġmodel Ġachieved Ġ70 % Ġaccuracy . ĠWe Ġare Ġcurrently Ġexperimenting Ġwith Ġdifferent Ġfeatures Ġtaking Ġadvantage Ġof Ġthe Ġannotation Ġavailable Ġin ĠNL I - PT Ġthus Ġreducing Ġtopic Ġbias Ġin Ġclassification . </s> In Ġfuture Ġwork Ġwe Ġwould Ġlike Ġto Ġinclude Ġmore Ġtexts Ġin Ġthe Ġdataset Ġfollowing Ġthe Ġsame Ġmethodology Ġand Ġannotation . </s> Acknowled gement </s> We Ġwant Ġto Ġthank Ġthe Ġresearch Ġteams Ġthat Ġhave Ġmade Ġavailable Ġthe Ġdata Ġwe Ġused Ġin Ġthis Ġwork : ĠCent ro Ġde ĠEst udos Ġde ĠL ingu ÃŃ st ica ĠGeral Ġe ĠA pl ic ada Ġat ĠUnivers id ade Ġde ĠCo imb ra Ġ( s pecially ĠCrist ina ĠMart ins ) Ġand ĠCent ro Ġde ĠL ingu ÃŃ st ica Ġda ĠUnivers id ade Ġde ĠLis boa Ġ( particularly ĠAm Ã¡ lia ĠMend es ). </s> This Ġwork Ġwas Ġpartially Ġsupported Ġby ĠFund a Ã§ Ã£o Ġpara Ġa ĠCi Ãª nc ia Ġe Ġa ĠTec n olog ia Ġ( post doctoral Ġresearch Ġgrant ĠS FR H / B PD / 10 99 14 / 2015 ).