Proceedings of the 46th Annual Meeting of the ACL

93 Pages·2010·2.7 MB·English

Coling 2010
23rd International Conference on
Computational Linguistics
Proceedings of the 2nd Workshop on
Cognitive Aspects of the Lexicon
Workshop chairs:
Michael Zock and Reinhard Rapp
22 August 2010
Beijing International Convention Center
Beijing, China
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ChineseInformationProcessingSocietyofChina
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Introduction
Wheneverwereadabook,writealetterorlaunchaqueryonasearchengine,wealwaysusewords,the
shorthandlabelsandconcreteformsofabstractnotions(concepts,ideasandmoreorlesswellspecified
thoughts). Yet,wordsarenotonlyvehiclestoexpressthoughts,theyarealsomeanstoconceivethem.
They are mediators between language and thought, allowing us to move quickly from one idea to
another, refining, expanding or illustrating our possibly underspecified thoughts. Only words have
theseuniquecapabilities,whichiswhytheyaresoimportant.
Obviously,agooddictionaryshouldcontainmanyentriesandalotofinformationassociatedwitheach
one of them. Yet, the quality of a dictionary depends not only on coverage, but also on accessibility
of information. Access strategies vary with the task (text understanding vs. text production) and the
knowledge available at the moment of consultation (words, concepts, speech sounds). Unlike readers
who look for meanings, writers start from them, searching for the corresponding words. While paper
dictionaries are static, permitting only limited strategies for accessing information, their electronic
counterparts promise dynamic, proactive search via multiple criteria (meaning, sound, related words)
andviadiverseaccessroutes. Navigationtakesplaceinahugeconceptuallexicalspace,andtheresults
are displayable in a multitude of forms (e.g. as trees, as lists, as graphs, or sorted alphabetically, by
topic,byfrequency).
Many lexicographers work nowadays with huge digital corpora, using language technology to build
and to maintain the lexicon. But access to the potential wealth of information in dictionaries remains
limitedforthecommonuser. Yet, thenewpossibilitiesofelectronicmediaintermsofcomfort, speed
andflexibility(multipleinputs,polyformoutputs)areenormous. Computationalresourcesarenotprone
tothesamelimitationsaspaperbounddictionaries. Thelatterwerelimitedinscope,beingconfinedto
a specific task (translation, synonyms, ...) for economical reasons, but this limitation is not justified
anymore.
Today,byexploitingtheadvantagesofthedigitalform,wecanperformalltasksviaonesingleresource,
which may comprise a dictionary, a thesaurus and even more. The goal of this second CogALex
workshop,whichfollowsthefirsteditionatCOLING2008inManchester,istoperformthegroundwork
for the next generation of electronic dictionaries, that is, to study the possibility of integrating the
differentresources,aswellastoexplorethefeasibilityoftakingtheusersneeds,knowledgeandaccess
strategiesintoaccount. Toreachthisgoal,wehaveinvitedresearchersfromfieldssuchascomputational
lexicography, psycholinguistics, cognitive psychology, language learning and ergonomics to address
oneorseveralofthefollowingtopics:
1. Conceptualinputofadictionaryuser. Whatisintheauthors’mindswhentheyaregeneratinga
messageandlookingforaword? Dotheystartfrompartialdefinitions,i.e. underspecifiedinput
(bag of words), conceptual primitives, semantically related words, something akin to synsets,
or something completely different? What does it take to bridge the gap between this input,
incompleteasitmaybe,andthedesiredoutput(targetword)?
2. Organizingthelexiconandindexingwords. Concepts,wordsandmulti-wordexpressionscanbe
organized and indexed in many ways, depending on the task and language type. For example,
in Indo-European languages words are traditionally organized in alphabetical order, whereas in
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Chinese they are organized by semantic radicals and stroke counts. The way words and multi-
word expressions are stored and organized affects indexing and access. Since knowledge states
(i.e. knowledgeavailablewheninitiatingsearch)varygreatlyandinunpredictableways,indexing
mustallowformultiplewaysofnavigationandaccess. Hencethequestion: whatorganizational
principlesallowthegreatestflexibilityforaccess?
3. Access,navigationandsearchstrategiesbasedonvariousentrytypes(modalities)andknowledge
states. Words are composed of meanings, forms and sounds. Hence, access should be possible
viaanyofthesecomponents: viameanings(bagofwords),viaforms,simpleorcompound(’hot,
dog’vs. ’hot-dog’), andviasounds(syllables). Accessshouldbepossibleevenifinputisgiven
in an incomplete, imprecise or degraded form. Furthermore, to allow for natural and efficient
access, we need to take the users’ knowledge into account (search space reduction) and provide
adequate navigational tools, metaphorically speaking, a map and a compass. How do existing
toolsaddresstheseneeds,andwhatcouldbedonetogofurther?
4. NLP applications: Contributors can also demonstrate how such enhanced dictionaries, once
embedded in existing NLP applications, can boost performance and help to solve lexical
and textual-entailment problems, such as those evaluated in SEMEVAL 2007, or, more
generally,generationproblemsencounteredinthecontextofsummarization,question-answering,
interactiveparaphrasingortranslation.
Quite a few of these issues are dealt with in the papers we received. The accepted papers present
a rich selection of ideas on the crossroads of semantics, cognition, lexicography, and language
learning, thereby emphasizing the interdisciplinary character of the workshop. These are the topics:
generating semantic networks, encoding commonsense knowledge in WordNet, textual entailment,
sentimentanalysis,corpus-basedextractionofconceptualclasses, parsingofthesauri,termextraction,
determining noun classifiers, requirements when using the dictionary of an authoring tool, and the
problemofwordaccess.
In sum, there is an active community of researchers working on cognitive aspects of the lexicon, and
thereisarealawarenessconcerningtheimportanceoftheproblemspresentedinourcallforpapers.
Wewouldliketothankallthepeoplewhoinonewayoranotherhavehelpedustomakethisworkshopa
success. OurspecialthanksgotoEduardHovyforhavingacceptedtogivetheinvitedpresentation,and
tothemembersoftheprogramcommitteewhodidanexcellentjobinreviewingthesubmittedpapers.
Their reviews were important not only to assure a good selection of papers, but also for the authors,
helping them to improve their work. We would also like to express our gratitude to the COLING
organizers,inparticulartothegeneralworkshopchairsandthepublicationchairs. Lastbutnotleast,we
wouldliketothankourauthorsfortheirpapersandpresentationsandtheparticipantsoftheworkshop
fortheirinterestandtheircontributionstothediscussions.
MichaelZockandReinhardRapp
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Organizers:
MichaelZock,LIF-CNRS,Marseille(France)
ReinhardRapp,UniversityofTarragona(Spain)
InvitedSpeaker:
EduardHovy,InformationSciencesInstitute,UniversityofSouthernCalifornia(USA)
ProgramCommittee:
SlavenBilac,GoogleTokyo(Japan)
PierretteBouillon,ISSCO,Geneva(Switzerland)
DanCristea,UniversityofIasi(Romania)
KatrinErk,UniversityofTexas(USA)
OlivierFerret,CEALIST(France)
ThierryFontenelle,EUTranslationCentre(Luxembourg)
SylvianeGrangerUniversite´ CatholiquedeLouvain(Belgium)
GregoryGrefenstette,Exalead,Paris(France)
UlrichHeid,IMS,UniversityofStuttgart(Germany)
ErhardHinrichs,UniversityofTu¨bingen(Germany)
GraemeHirst,UniversityofToronto(Canada)
EduardHovy,ISI,UniversityofSouthernCalifornia,LosAngeles(USA)
Chu-RenHuang,HongKongPolytechnicUniversity(China)
TerryJoyce,TamaUniversity,Kanagawa-ken(Japan)
PhilippeLanglais,DIRO/RALIUniversityofMontreal(Canada)
Marie-ClaudeL’Homme,UniversityofMontreal(Canada)
VerginicaMititelu,RACAI,Bucharest(Romania)
AlainPolgue`re,ATILF-CNRS/Universite´ Nancy2(France)
ReinhardRapp,UniversityofTarragona(Spain)
SabineSchulteimWalde,UniversityofStuttgart(Germany)
GillesSe´rasset,IMAG,Grenoble(France)
SergeSharoff,UniversityofLeeds(UK)
AnnaSinopalnikova,FIT,BUT,Brno(CzechRepublic)
CaroleTiberius,InstituteforDutchLexicology(TheNetherlands)
TakenobuTokunaga,TITECH,Tokyo(Japan)
DanTufis,RACAI,Bucharest(Romania)
PiekVossen,VrijeUniversiteitAmsterdam(TheNetherlands)
YorickWilks,OxfordInternetInstitute(UK)
MichaelZock,LIF-CNRS,Marseille(France)
PierreZweigenbaum,LIMSI-CNRS,Orsay(France)
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Table of Contents
DistributionalSemanticsandtheLexicon
EduardHovy...........................................................................1
SemanticNet-PerceptionofHumanPragmatics
AmitavaDasandSivajiBandyopadhyay..................................................2
ExploitingLexicalResourcesforTherapeuticPurposes: theCaseofWordNetandSTaRS.sys
GianlucaE.LebaniandEmanuelePianta ................................................ 12
TextualEntailmaintRecognitionusingWordOverlap,MutualInformationandSubpathSet
YukiMuramatsu,KunihiroUdukaandKazuhideYamamoto...............................18
TheColorofEmotionsinTexts
CarloStrapparavaandGozdeOzbal.....................................................28
HowtoExpandDictionariesbyWeb-MiningTechniques
NicolasBe´chetandMathieuRoche......................................................33
AnOptimalandPortableParsingMethodforRomanian,French,andGermanLargeDictionaries
NeculaiCurteanu,AlexMoruzandDianaTrandabat......................................38
ConceptualStructureofAutomaticallyExtractedMulti-WordTermsfromDomainSpecificCorpora: a
CaseStudyforItalian
ElisaLavagninoandJungyeulPark......................................................48
Computational Lexicography: A Feature-based Approach in Designing an E-dictionary of Chinese
Classifiers
HelenaGao...........................................................................56
InSearchofthe’Right’Word
StellaMarkantonatou,AggelikiFotopoulou,MariaAlexopoulouandMariannaMini.........66
LexicalAccess,aSearch-Problem
MichaelZock,DidierSchwabandNirinaRakotonanahary.................................75
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Conference Program
Sunday,August22,2010
9:00–9:15 OpeningRemarks
InvitedKeynotePresentation
9:15–10:30 DistributionalSemanticsandtheLexicon
EduardHovy
10:30–11:00 Coffeebreak
Session1: SemanticsandCognition
11:00–11:30 SemanticNet-PerceptionofHumanPragmatics
AmitavaDasandSivajiBandyopadhyay
11:30–12:00 Exploiting Lexical Resources for Therapeutic Purposes: the Case of WordNet and
STaRS.sys
GianlucaE.LebaniandEmanuelePianta
12:00–12:30 TextualEntailmaintRecognitionusingWordOverlap,MutualInformationandSub-
pathSet
YukiMuramatsu,KunihiroUdukaandKazuhideYamamoto
12:30–13:00 TheColorofEmotionsinTexts
CarloStrapparavaandGozdeOzbal
13:00–14:00 Lunchbreak
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Sunday,August22,2010(continued)
Session2: Lexicography
14:00–14:30 HowtoExpandDictionariesbyWeb-MiningTechniques
NicolasBe´chetandMathieuRoche
14:30–15:00 AnOptimalandPortableParsingMethodforRomanian,French,andGermanLargeDic-
tionaries
NeculaiCurteanu,AlexMoruzandDianaTrandabat
15:00–15:30 ConceptualStructureofAutomaticallyExtractedMulti-WordTermsfromDomainSpecific
Corpora: aCaseStudyforItalian
ElisaLavagninoandJungyeulPark
15:30–16:00 Coffeebreak
Session3: WordAccessandLanguageLearning
16:00–16:30 ComputationalLexicography: AFeature-basedApproachinDesigninganE-dictionaryof
ChineseClassifiers
HelenaGao
16:30–17:00 InSearchofthe’Right’Word
StellaMarkantonatou,AggelikiFotopoulou,MariaAlexopoulouandMariannaMini
KeynotePresentation
17:00–17:45 LexicalAccess,aSearch-Problem
MichaelZock,DidierSchwabandNirinaRakotonanahary
17:45–18:00 WrapUpDiscussion
18:00 EndoftheWorkshop
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INVITED KEYNOTE PRESENTATION

Distributional Semantics and the Lexicon

Eduard Hovy
Information Sciences Institute
University of Southern California
[email protected]

The lexicons used in computational linguistics larly accompany it (in the classic words of
systems contain morphological, syntactic, and Firth, “you shall know a word by the company
occasionally also some semantic information it keeps”). And this is true not only for indi-
(such as definitions, pointers to an ontology, vidual wordsenses, but also for larger units such
verb frame filler preferences, etc.). But the as topics: the product of LDA and similar topic
human cognitive lexicon contains a great deal characterization engines is similar.
more, crucially, expectations about how a In this talk I argue for a new kind of seman-
word tends to combine with others: not just tics, which is being called Distributional Se-
general information-extraction-like patterns, mantics. It combines traditional symbolic logic-
but specific instantial expectations. Such in- based semantics with (computation-based)
formation is very useful when it comes to lis- statistical word distribution information. The
tening in bad aural conditions and reading core resource is a single lexico-semantic lexicon
texts in which background information is that can be used for a variety of tasks, provided
taken for granted; without such specific ex- that it is reformulated accordingly. I show how
pectation, one would be hard-pressed (and to define such a semantics, how to build the
computers are completely unable) to form co- appropriate lexicon, how to format it, and how
herent and richly connected multi-sentence to use it for various tasks. The talk pulls
interpretations. together a wide range of related topics,
Over the past few years, NLP work has in- including Pantel-style resources like DIRT,
creasingly treated topic signature word distri- inferences / expectations such as those used in
butions (also called ‘context vectors’, ‘topic Schank-style expectation-based parsing and
models’, etc.) as a de facto replacement for expectation-driven NLU, PropBank-style word
semantics. Whether the task is wordsense dis- valence lexical items, and the treatment of
ambiguation, certain forms of textual entail- negation and modalities. I conclude by arguing
ment, information extraction, paraphrase that the human cognitive lexicon has to have the
learning, and so on, it turns out to be very use- same kinds of properties as the Distributional
ful to consider a word(sense) as being defined Semantics lexicon, given the ways people do
by the distribution of word(senses) that regu- things with words.

1
Proceedingsofthe2ndWorkshoponCognitiveAspectsoftheLexicon(CogALex2010),page1,
Beijing,August2010
SemanticNet-Perception of Human Pragmatics
Amitava Das1 and Sivaji Bandyopadhyay2
Department of Computer Science and Engineering
Jadavpur University
[email protected] [email protected]

technical. It is often used in ordinary language
Abstract
to denote a problem of understanding that
comes down to word selection or connotation.
SemanticNet is a semantic network of
We studied with various Psycholinguistics ex-
lexicons to hold human pragmatic
periments to understand how human natural
knowledge. So far Natural Language
intelligence helps to understand general se-
Processing (NLP) research patronized
mantic from nature. Our study was to under-
much of manually augmented lexicon
stand the human psychology about semantics
resources such as WordNet. But the
beyond language. We were haunting for the
small set of semantic relations like
intellectual structure of the psychological and
Hypernym, Holonym, Meronym and
neurobiological factors that enable humans to
Synonym etc are very narrow to cap-
acquire, use, comprehend and produce natural
ture the wide variations human cogni-
languages. Let’s come with an example of
tive knowledge. But no such informa-
simple conversation about movie between two
tion could be retrieved from available
persons.
lexicon resources. SemanticNet is the Person A: Have you seen the
attempt to capture wide range of con-
movie ‘No Man's Land’? How
text dependent semantic inference
is it?
among various themes which human Person B: Although it is
beings perceive in their pragmatic
good but you should see
knowledge, learned by day to day cog- ‘The Hurt Locker’?
nitive interactions with the surrounding
May be the conversation looks very casual,
physical world. SemanticNet holds
but our intension was to find out the direction
human pragmatics with twenty well es-
of the decision logic on the Person B’s brain.
tablished semantic relations for every
We start digging to find out the nature of hu-
pair of lexemes. As every pair of rela-
man intelligent thinking. A prolonged discus-
tions cannot be defined by fixed num-
sion with Person B reveals that the decision
ber of certain semantic relation labels
logic path to recommend a good movie was as
thus additionally contextual semantic
the Figure 1. The highlighted red paths are the
affinity inference in SemanticNet could
shortest semantic affinity distances of the hu-
be calculated by network distance and
man brain.
represented as a probabilistic score.
We call it semantic thinking. Although the
SemanticNet is being presently devel-
derivational path of semantic thinking is not
oped for Bengali language.
such easy as we portrait in Figure 1 but we
keep it easier for understandability. Actually a
1 Historical Motivation
human try to figure out the closest semantic
affinity node into his pragmatics knowledge by
Semantics (from Greek "σηµαντικός" - seman-
natural intelligence. In the previous example
tikos) is the study of meaning, usually in lan-
Person B find out with his intelligence that No
guage. The word "semantics" itself denotes a
Man's Land is a war movie and got Oscar
range of ideas, from the popular to the highly
2
Proceedingsofthe2ndWorkshoponCognitiveAspectsoftheLexicon(CogALex2010),pages2–11,
Beijing,August2010