Joaquim Moré
[email protected]ComputationalLinguist, OfficeofLearning Technologies, OpenUniversityofCatalonia (UOC)
Salvador Climent
[email protected]Lecturer, ArtsandHumanitiesDepartment, OpenUniversityofCatalonia (UOC)
Marta Coll-Florit
[email protected]Lecturer, ArtsandHumanitiesDepartment, OpenUniversityofCatalonia (UOC)
An Answering System
for Questions Asked by Students in
an e-Learning Context
ARTICLE
Submitted in: May 2011 Accepted in: February 2012 Published in: July 2012
Abstract
Inthisarticle, wepresentasystemthathelpstutorstoanswer questionsaskedbytheirstudentsatan
onlineuniversity:theOpenUniversityofCatalonia (UOC). CommunicationbetweenUOCstudents
andtheirtutorsisfullyonline; studentsask questionsandtutorsanswerthembye-mail. Thesystem,
whichiscurrentlybeingdevelopedattheUOC’sOfficeofLearning Technologies (OLT), aimsto find
multilingualcontextswithusefulinformationtoenabletutorstogivefast, appropriateanswersto
students. Thesecontextsareextractedfromcourselearningmaterials, frompreviousmessagespos
-tedonsubjectdiscussionboards, andalsofromarticlesandothersourcesofinformationavailableon
Recommended citation
MORÉ, Joaquim; CLIMENT, Salvador; COLL-FLORIT, Marta (2012). “An AnsweringSystemforQuestions Asked
byStudentsinane-LearningContext” [onlinearticle]. Universities and Knowledge Society Journal (RUSC).
Vol. 9, No 2, pp. 229-239 UOC. [Accessed:dd/mm/yy].
<http://rusc.uoc.edu/ojs/index.php/rusc/article/view/v9n2-more-climent-coll-florit/v9n2-more-climent
-coll-florit-eng>
<http://dx.doi.org/10.7238/rusc.v9i2.1161>
1. Introduction
Atan onlineuniversity, tutors are faced withthe arduous task of writing answers —containing
detailed, usefulinformation— toevery queryand questiontheyreceivedailyfromtheirstudents. The
taskcantakeupaconsiderableamountofthetimetheyspendeachdayonteachingifthenumber
ofstudentsishighandthetutorshaveanarrowtimeframeinwhichtoreplytothemall.
In most question-answeringsystemsfore-learningtutors (Hungetal., 2005; Fengetal., 2006;
Wang et al., 2006; Yang, 2009), questions are answered automatically, but this approach has to
overcome anumberofproblems. Thisincludesthe automaticrecognitionofa questionwhen a
studentasks itimplicitlyratherthanexplicitly, withdeviationsfrom formal, normativeexpression.
Forexample, spellingmistakesortypographicalerrorsarecommonplaceine-mails, asareunstable
syntaxandotherphenomena, thusmakingthe questionmoredifficulttoidentify. Anotherproblem
issearchingforananswertoa questionthat, despitebeingthematicallyrelatedtothesubject, does
notrefertoanaspectcoveredbycourselearningmaterials. Inthiscase, traditionalsolutionsbasedon
informationcontainedinlearningmaterialsareofnouse, sincesuchsolutionsrelyontheextraction
ofinformationfromadatabasewith question-answerpairs, orfromanannotatedcorpus (Fenget
al., 2006; Wangetal., 2006; Yang, 2009). A solutionthataimstoextendbeyondlearningmaterialsis
theretrievalofananswertoa questionaskedbyastudentinanopenonlinecommunity (Bernhard
theInternet. Apartfromhelpingtutorsto findbetteranswers, thesystemisalsousefulforupdating
theirknowledgeandcontributingtotheirlifelonglearning.
Keywords
question-answeringsystems; e-learning; speechacts; tutor
Un sistema de respuestas a consultas formuladas por alumnos
en un contexto de aprendizaje virtual
Resumen
En este artículo presentamos un sistema que ayuda a los docentes a responder las preguntas de sus alum-nos en una universidad virtual, concretamente la Universitat Oberta de Catalunya (UOC). La comunicación entre alumno y docente se realiza de forma totalmente virtual: las preguntas y las respuestas se formulan y contestan mediante correo electrónico. El sistema, que se está desarrollando en el Área de Tecnología Educativa de la UOC, tiene como principal objetivo encontrar contextos multilingües con información útil para responder al estudiante de forma rápida y adecuada. Los contextos se extraen de los materiales del curso, los foros de participación de la asignatura, artículos y otras fuentes de información disponibles en internet. Además de ayudar a los docentes a encontrar mejores respuestas, el sistema también es útil para actualizar sus conocimientos y desarrollar su aprendizaje permanente.
Palabras clave
& Gurevych, 2008). Theproblemwiththis, however, isthattheanswersreturnedmaybeabsurdand
veryunreliable. Consequently, thesystemwouldhavetolearntodiscriminatebetweengoodand
badanswers, andthisissimplytoodemandingforcurrentsystems.
Besidessuchproblems, whichpartially accountforatutor’slackoftrustinautomatic question
-answeringengines, thesesystemsdonotcontemplateanimportantaspectthatweidentifiedinthe
UOCtutors’messaging. Questionsoftenprompttutorstosearchforthelatestinformation; bydoingso,
theyupdatetheirknowledge. Many questionsarisefromastudent’sreflectionsonanexerciseoron
recommendedreadingand, asaresult, tutorshaveto findanansweronanaspectthattheyhadnot
consideredor, quitesimply, thattheywereunawareof. Studentsthusfostertheirtutors’lifelonglearning.
Inthisarticle, wepresentahelptoolfortutors, theaimofwhichisnottogetanexactanswer
toa question, butratherto findcontexts withusefulinformation toenablethemtogive afast,
appropriate answertostudents. Thisaim allows forthe development ofa method thatis more
flexiblethanthetraditionaloneforanswersearchsystems. Thebestresultisobtainedwhenthere
isagooddirect questionamongthecontextsthatthesystemhasfound. Thesystemcutsdown
oninformation searchtimes, allows tutorstoupdatetheirknowledgeandisusefulfor assessing
students’contributiontotheacquisitionofinformationbytheirfellowstudentsandalsotheirtutors.
Thisarticleisstructuredinthefollowingmanner. Sectiontwopresentsthemethodology, whichis
basedonapragmatictheory. Sectionsthreeandfourdescribetheprototypethatwehavedeveloped
thusfarandpresentanevaluationofit. The finalsectioncontainstheconclusionsandfuturework.
2. Methodology
Our system’s users are the virtual classroom tutors for all of the UOC’s bachelor’s degrees and
programmes. Accordingly, wehavedevelopedamethodology thatisindependent fromspecific
thematic domains. We decided to approach the problemby positioning itwithin a theoretical
framework, in this instance Searle’s theory of speech acts (1969), which describes the basesof
communicationbetweenaspeakerandahearer.
In a communicative situation where tutors and students interact by e-mail, students have
objectivesthattheyexpecttobemetwiththetutors’help. It isthereforecrucialfor studentsto
formulatespeechactswhoselinguistictraitsclearlyindicatetheirexpectationstotutors. Conversely,
speechactsthattutorsformulateintheiranswersmustcontainlinguistictraitsconfirmingthatthey
meetstudents’expectations.
A speechact consists of twoelements. The firstis thespeech act expression (SAE); itisthe
expressionbywhichtheheareridentifiesthespeaker’sexpectations.
“Idon’tunderstand” isanexampleofthewayinwhichastudentexpressesanexpectationthat
someonewillclarifyaconcept.
Thesecondelementisaspeechactobject (SAO); itisthekeyterm (orterms) ofthespeaker’s
speechact. Forexample, ifastudentsays “Idon’tunderstandthenotionofhyponymy,” thenhyponymy
Our hypothesisisthatdocumentsegmentsthatareusefultotutorsarethosecontainingthe
SAOsofamessage. The questionwethereforeaskedourselveswasthis:towhatextentdotheSAOs
ofamessagefoundinadocumentcontributetoour system’susefulness? Withthisinmind, the
prototypethatwehavedevelopedsearchesforcontextsinreliablesourcesofinformationinwhich
SAOsco-occur. SAOcandidatesareidentifiedautomatically. However, itisuptothetutorstoselect
themostrelevantones, sincetheyarecapableofworkingoutstudents’intentionsdespitethefuzzy
discourserelationshipsthatarecharacteristicofinformale-mails.
3. Prototype
Thusfar, wehavedevelopedaprototypethatsearchesforcontextsinwhichrelevanttermscontained
inastudent’smessageco-occur. Inthissection, weshallpresenttheprototypeprocedureandthe
sourcesofinformationthatitconsults.
Theprototypeprocedurehasfourstages, asshownbelow:
1. Extraction of the subject of the message
The subjectofthe messageisextractedin ordertoretrieve other messagesfromdiscussion
boardswiththesameorasimilarsubject.
2. Automatic morphological analysis of the body text of the message
ThesystemmorphologicallyanalysesthebodytextofthemessageusingtheFreeLingparser
(Atseriasetal., 2006). MostofthemessagesarewritteninCatalan, soitissetasthedefaultsource
language.
3. Tag cloud generation
Thesystempresentsatagcloudwiththerelevantconceptsfoundinastudent’smessagesothat
tutorscanselecttheSAOs, thatistosay, theconceptsthattheywanttofocusoninorderto find
usefulcontextsandthusbeabletogiveagoodanswer. Thetagcloudisgeneratedbyanautomatic
terminologyextractor. Expressionsbetween quotationmarks, verbsandnounsareextracted. Terms
probably belonging to the conceptual domain of the subject of the message are highlighted.
However, inordertoidentifytheseterms, asystemthatisindependentfromconceptualcontent
has been used. The system consults theopen-source Catalan-English DACCO dictionary (http://
sourceforge.net/projects/dacco/), which includes information about the frequency of its entries
intermsofthenumberofresultsreturnedbyGoogle. Ifwestartfromthehypothesisthatterms
relatedtoaspecificconceptualdomainreturnfewerresultsthangeneral-vocabularyterms, thenthe
highlightedtermsarethosethatfallbelowanumericthresholdofresults.
4. Useful contexts search
Afterselecting theSAOsfromthetagcloud, thesystemsearchesforcontextsin Catalanand
Englishinwhichthedenominationsoftheselectedobjectsco-appearinbothlanguages. Werefer
tothesecontextsasuseful context candidates (UCCs). Thesourcesofinformationconsultedforthe
R Messagesonsubjectdiscussionboards, writteninprevioussemesters:thesame questionmay
havebeenaskedinaprevioussemester, towhichatutororastudentmayhavegivenagood
answer.
R Subjectlearningmaterials:thesystemusesalearningmaterialssearchenginedevelopedby
theUOCtofindlearningmaterialscontextsinwhichtheselectedconceptsco-appear.
R Wikipedia:linkstoWikipediainCatalanandEnglish, wherethetermsselectedbyatutorare
explained.
R Onlinescholarlyarticles:fortheprototype, thesystemusedtheDelicious (http://www.delicious.
com) searchenginetofindarticlesinCatalanandEnglishwhosetagsmatchedtheconcepts
selectedbyatutor. ThesystemalsousedthesearchengineofCiteULike (http://www.citeulike.
org/), whichisafreeonlineservicethatclassifiesscholarlypublicationsandretrievesarticlesby
thesamemethod. So, theprototypeshowedtheresultspagesfromDeliciousandCiteULike,
withlinkstoarticlescoveringtopicsrelatedtotheselectedterms.
Otherfreescholarlyservicescanbeaddediftutorsconsiderthattobenecessarytoobtaingood
answeringcontexts.
4. Evaluation
Theprototypewasevaluatedtoobtaintwotypesofinformation: first, theprototype’susefulness
intermsofenablingtutorsto findandgiveappropriate, fastanswerstostudents, andsecond, the
contributionofeachsourceofinformationtotheprototype’susefulness. A numberofelementsthat
couldbeimprovedwerealsoidentified.
4.1. Evaluation procedure
Fortheevaluation, thesubjectGeneralLinguisticsIIwaschosenandtwogroupsofevaluatorswere
formed. The firstgroup, calledexperts, comprisedthreesubjectconsultants. Thesecondgroup, called
novices, comprisedthreesubjectspecialiststhathadnothadanyexperienceasUOCconsultants.
Consequently, wewereabletoevaluatewhethertheprototypewasmoreusefulfornovicesthanfor
experts, andviceversa.
Fortymessageswereselectedfor theevaluation. Therearetwoexplanationsfor thenumber
ofmessagesandthesemesterstheycover: first, thediscussionboarddatabaseonlyincludedthe
lasttwosemesters, andsecond, weruledoutany messagesthat, intheopinionofatleast three
evaluators, weretoodecontextualisedorunspecifictodrawanyusefulcontextsfrom.
A webenvironmentwasused, whichwasorganisedasfollows:
R A space setaside forselecting the message thatan evaluatorwanted toview. This space containedalistofnumbersfrom1to40, eachofthembeingamessagereferencenumber.
R A spacesetasideforselectingtheSAOsfromatagcloudoftermsinthemessage. Afterselecting
theSAOs, theevaluatorsclickedabuttontostarttheprototype’ssearchforrelevantcontextsin
ordertogiveananswer.
TheprototypedisplayedtheUCCsfoundineachsourceofinformation, inaccordancewiththe
termsselectedfromthetagcloud. Thetaskforbothexpertandnoviceevaluatorswastoscorethe
usefulnessofthesourceofinformationaccordingtotheUCCsdisplayed. Theevaluatorsrecorded
theirscoresonaspreadsheetorganisedbythemessage-sourceofinformationrelationship. If, ona
subjectdiscussionboardforexample, anevaluatorfoundthatananswergivenbyastudenttothe
same questionwasveryuseful, thenthatevaluatorgavethehighestscoretothe ‘subjectdiscussion
board’source.
Theusefulness ofthecontexts hadtwodimensions. The first wastheusefulnessofasource of
informationintermsofgivingagoodanswer (UGA). Thesecondwastheusefulnessofasourceof
informationintermsofgivingafastanswer (UFA). Theitemswerescoredonascaleof fivevalues:0
(Notuseful), 1 (Notveryuseful), 2 (Useful), 3 (Veryuseful) andNC (nocontext). Thelatterwasusedwhen
thesystemwasunabletoretrieveanyUCCsfromthesourcesofinformation. Inaddition, theevaluators
wereencouragedtowritecommentsontheeffortanddifficultyinvolvedinobtainingusefulcontexts.
Thesecomments providedus with veryuseful pointers as tohow wecould improve thesystem.
The analysis evaluation procedure was divided into two stages; a macroevaluation and a
microevaluation. Themacroevaluationwasananalysisoftheresultsrelatedtothesystem’susefulness,
thatistosay, itsusefulnessintermsofgivingagoodanswerandafastanswer. Thepurposeofthe
microevaluationwastodeterminethecontributionofeachsourceofinformationtothesystem’s
usefulness, thusallowinganyaspectthatcouldbeimprovedtobeidentified.
4.2. Macroevaluation
Wewantedtocomparetheexperts’andnovices’perceptionsofusefulnessintermsofgivingagood
answer. Firstofall, wecalculatedeachgroupmember’sperception. Foreachmessage, wecollected
thescoreofthemosthighlyvaluedsourceofinformation. Thenwecalculatedtheevaluator’smean
score (EMS), whichwasthemeanofthehighestscores. Thegroupmembers’perceptionofusefulness
wasthemeanoftheEMSsofthethreeevaluators. Thiswashowwecomparedthemeanofthe
experts’EMSsandthemeanofthenovices’EMSs.
Wealsowantedtocomparetheexperts’andnovices’perceptionsofusefulnessintermsofgiving
agoodanswer quickly. TheEMSsofeachgroupwerecalculatedasexplainedabove, butonthis
occasiononthescoresforthesystem’sspeed.
4.3. Microevaluation
We alsowantedtocomparetheexperts’andnovices’perceptionsofthe most usefulsourcesof
information. Aswasthecaseforthemacroevaluation, we firstofallcalculatedeachgroupmember’s
forallthemessages. Theresultwasthemeanscoreoftheusefulness (MSU) ofeachsourceaccording
toeachevaluator. BycalculatingthemeanoftheMSUsforthethreeevaluatorsofeachgroup, we
obtainedthegroup’sperceptionofthesources’contributiontothesystem’susefulness. Thus, we
wereabletocomparetheexperts’andnovices’perceptions. Theevaluators’commentswerealso
usedforthemicroevaluation.
4.4. Results
Thesystem’susefulnessintermsof findinginformationwasscoredslightlyhigherbytheexperts
(1.77onascaleof0to3) thanbythenovices (1.51). However, theexpertsgavelowerscoresfor
usefulnessintermsofgivingafastanswer (1.47) thanthenovices (1.75).
Thegroupofexpertsconsideredthatsnippetsfromwebpagesmadeabettercontributionto
givingagoodanswer, alongwayaheadofothersourcesofinformation. Forthegroupofnovices,
snippetsfromwebpagesandWikipediaarticlesscoredhigher, thoughthedistancebetweenthem
andothersourcesofinformationwasmuchshorter.
Accordingtotheexperts’comments, web pageswereusefulfor givingananswer, though it
meantthattheyhadtospendalotoftime findingthemostsuitablecontext. Inaddition, theysaid
thattheyfoundusefulcontextsafterperformingmorethanonetest. Inotherwords, theyhadto
selectdifferenttermsfromthetagcloud. Tosomeextent, thisexplainswhythegroupofexpertsgave
lowscoreswhenevaluatingthesystem’susefulnessintermsofgivingafastanswer.
The contextsdisplayed bythelearning materials searchenginewerethesecondmost highly
valuedsourceofinformationbythegroupofexperts, whereasthenovicesgaveahigherscoreto
Wikipedia. Itwouldthereforeseem thattheranking differencesare duetothegroupofexperts’
greaterexperienceofsearchingforinformationrelatedtothesubjectbyusingsearchenginesandto
theirabilitytocombinekeywordstoobtainusefulresults.
Articleswereatthebottomoftheranking; thesewerescoredlowerthan1.5bytheexpertsand
novicesalike. Accordingtoacommentbyanevaluator, thiscouldbeduetothefactthatthearticles
foundinDeliciousandCiteULikedealwithveryspecialisttopics, andtheirtargetaudiencebasically
consistsoflecturersandgraduatestudents. Conversely, thetopicscoveredinWikipediaarebetter
suitedtothe questionsandreflectionsofundergraduatestudents.
Anotherissueweidentifiedwastherelationshipbetweenthetypesofstudentexpectationand
thesourcesofinformation. Forexample, courselearningmaterialsareusefulforclarifyingaconcept,
thoughstudentsgenerallyaskaboutinformation thatisnotincludedinthematerials. Moreover,
Wikipediareferencesand links toexternalonline resourcesare usefulfor findingasolutiontoa
problemorforsuggestingadditionalreading. Wikipediaarticlesandwebpagesalsoprovideextra
informationthatcomplementslearningmaterialsandhelpsstudentstoconfirmthattheirreflections
—andeventheirdigressions— areontherighttrack. Inaddition, suchsourcesofinformationare
usefulforupdatingatutor’sknowledge. Iftheycovertopicsincludedinsubjectlearningmaterials,
evenscholarlyarticlescancontributetoatutor’slifelonglearning.
Previous messages on subjectdiscussion boards match messages postedby students asking
thehistoryofasubject. Inaddition, previousmessagescannotberetrievedif, whenwritingthem,
studentsuseunder-specifiedreferencesthatonlymakesenseatthetimeofwriting.
5. Conclusions
Inthisarticle, wehavepresentedatutorassistantwhosemethodologydistinguishesitfromtraditional
approaches:thesystemis flexibleenoughtobeabletodealwithcommunicativeobjectivesthatgo
beyondansweringstudents’ questions. Whenmessagescontaindigressionsorreflectionsevokedby
readinglearningmaterials, thesystempromotesa ‘learnfromyourstudents’learningprocess. Sucha
processbecomesevidentwhenatutor findsthatastudenthasgivenagoodansweronaprevious
discussionboard.
Theresultsoftheprototypeevaluationarepromising, bearinginmindnotonlytheshortperiod
oftimecoveredbythecorpusevaluated, butalsothefactthatmuchdependsonhowrecurrenta
question, digressionor reflectionis. However, themethodologythatwehavedevelopedthusfar
requiresalotoftimetobespentonsearchingforinformationandisthereforeslightlymorebeneficial
forthegroupofexperttutors. Forthatreason, ouraimistoimprovethesystem’susefulnessinterms
ofgivingafastanswerontheonehand, andofgivingagoodanswerwithoutanydistinguishable
differencebetweenexpertsandnovicesontheother.
Weintendtoimproveitsusefulnessintermsofgivingafastanswerbybroadeningthepragmatic
approachandtakingadvantageoftherelationshipbetweenstudents’expectationsandsourcesof
information. Inaddition, accountwillbetakenoftherelationshipbetweenastudent’sspeechact
expressionandthesnippetoftextthatbestmatchesthatstudent’sexpectation. Moreover, weintend
tomakethesearchforusefulcontextseasier, withoutanydistinguishabledifferencebetweenexperts
andnovices, byexpandingkeywords. Whatwemeanbythisisthatthetermsselectedbyauserwill
activatesemanticallyrelatedterms, eveniftheyarenotvisibleinthetagcloud. Finally, weplanto
integrateasearchengineforscholarlyarticleswithusefulcontentforundergraduatestudentsand
tutorsalike. Doctoraltheses, andtheirstate-of-the-artsectionsinparticular, areinterestingcandidates.
Acknowledgments
Thisresearchwasfundedbytheproject KNOW2 (TIN2009-14715-C04) oftheSpanishMinistryof
ScienceandInnovationandbytheprogramme APLICA 2010oftheOpenUniversityofCatalonia.
References
ATSERIAS, Jordi; CASAS, Bernardino; COMELLES, Elisabet; GONZÁLEZ, Meritxell; PADRÓ, Lluís; PADRÓ,
Proceedings of the fifth international conference onLanguage Resources and Evaluation (LREC 2006),
ELRA. Genoa, Italy.
BERNHARD, Delphine; GUREVYCHG, Iryna (2008). “AnsweringLearner’sQuestionParaphrasesfromSo
-cialQ&A Sites”. In:Proceedings of the 3rd Workshop on Innovative Use of NLP for Building Educational
Applications, ACL. Columbus, Ohio. Pages44-52.
FENG, Dpnghui; SHAW, Erin; KIM, Jihie; HOVY, Edward (2006). “AnIntelligentDiscussion-botfor Answe
-ringStudent queriesinthreadeddiscussions”. In:Proceedings of Intelligent User Interfaces.
HUNG, JasonC.; WANG, Ching-Sheng; YANG, Che-Yu; CHIU, Mao-Shuen; YEE, George (2005). “Applying
WordSenseDisambiguationtoQuestion-AnsweringSystemfore-Learning”. In:Proceedings of the
IEEE 19th International Conference on Advanced Information Networking and Applications. Taiwan.
Pages157-162.
SEARLE, John (1969). Speech Acts. An Essay in the Philosophy of Language. Cambridge.
WANG, Chun-Chia; HUNG, JasonC.; SHIH, Timothy K.; LIN, Hsiau-Wen (2006). “A Repository-based
Question AnsweringSystemforCollaborativeE-learning”. Journal of Computers.Vol. 17, No3, pa
-ges55-58.
YANG, Che-You (2009). “A SemanticFAQSystemforOnlineCommunity”. Journal of Software. Vol. 4, No
2, pages153-158.
About the Authors
Joaquim Moré
ComputationalLinguist, OfficeofLearning Technologies, OpenUniversityofCatalonia (UOC)
JoaquimMoré holdsabachelor’sdegreeinEnglishandmaster’sdegreeinComputationalLinguistics
awardedbytheUniversityofBarcelona. HecurrentlyworksasacomputationallinguistintheOffice
ofLearning TechnologiesattheUOC. Inthe fieldofeducation, hespecialisesintheretrievalofinfor
-mationfromtheInternettofacilitateteachingandlearningtasks, andtoensurebothlecturers’and
students’lifelonglearning. Inother fields, hespecialisesintranslationtechnologies. Heisamember
oftheconsolidatedresearchgroupGRIAL (Inter-universityResearchGroupforLinguistic Applica
-tions) andaresearcherattheInternetInterdisciplinaryInstitute – UOC. Heisdoinghisdoctoralthesis
Marta Coll-Florit
Lecturer, ArtsandHumanitiesDepartment, OpenUniversityofCatalonia (UOC)
MartaColl-FloritholdsadoctorateintheInformationand KnowledgeSociety (InternetInterdiscipli
-naryInstitute – UOC), amaster’sdegreeinCognitiveScienceandLanguage, andabachelor’sdegree
inGeneralLinguistics (UniversityofBarcelona). SheiscurrentlyalecturerinLinguisticsinthe Arts
andHumanitiesDepartmentattheUOC. Sheisaspecialistinappliedlinguistics (computational
linguistics, cognitivelinguisticsandpsycholinguistics). Sheisamemberoftheconsolidatedresearch
groupGRIAL (Inter-universityResearchGroupforLinguistic Applications) andamemberoftheboard
ofSCOLA (SpanishCognitiveLinguistics Association).
UniversitatObertadeCatalunya (UOC)
Avingudadel Tibidabo, 45-47
08035 Barcelona Spain
Salvador Climent
Lecturer, ArtsandHumanitiesDepartment, OpenUniversityofCatalonia (UOC)
SalvadorClimentholds adoctorateinRomanceLanguages (Cognitivescienceandlanguage), a
master’sdegreeinComputationalLinguisticsandabachelor’sdegreeinSpanishawardedbytheUni
-versityofBarcelona. HeiscurrentlyalecturerinLinguisticsinthe ArtsandHumanitiesDepartmentat
theUOC, anddirectoroftheCatalanLanguageandLiteratureprogramme. Heisaspecialistincom
-putationallinguisticsandcognitivelinguistics, amemberoftheconsolidatedresearchgroupGRIAL
(Inter-universityResearchGroupforLinguistic Applications) anddirectoroftheLanguageProcessing