Argumentation mining PDF
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Series ISSN 1947-4040 S T E D E • S C H N E Series Editor: Graeme Hirst,University of Toronto I D E R Argumentation Mining Argumentation Manfred Stede, University of Potsdam Jodi Schneider, University of Illinois at Urbana-Champaign Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major Mining conferences and journals of the field. Its goals are to identify argumentation in text or dialogue; to construct representations of the constellation of claims, supporting and attacking moves (in different levels of detail); and to characterize the patterns of reasoning that appear to license the argumentation. Furthermore, recent work also addresses the difficult tasks of evaluating the persuasiveness A and quality of arguments. Some of the linguistic genres that are being studied include legal text, student essays, political discourse R G and debate, newspaper editorials, scientific writing, and others. U The book starts with a discussion of the linguistic perspective, characteristics of argumentative language, and their relationship M to certain other notions such as subjectivity. E N Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is T on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally A T very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long I O term, and therefore we also touch upon some fundamentals of reasoning approaches. N Then the book turns to its focus, the computational side of mining argumentation in text. We first introduce a number of M annotated corpora that have been used in the research. From the NLP perspective, argumentation mining shares subtasks with I research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many N I technical approaches are being borrowed from those (and other) fields.We break argumentation mining into a series of subtasks, N G starting with the preparatory steps of classifying text as argumentative (or not) and segmenting it into elementary units.Then, central steps are the automatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation of statements. Next, we discuss a few steps that try to ‘dig deeper’: to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called ‘enthymemes’), and to evaluate the quality of the argumentation.We also take a brief look at ‘the other Manfred Stede side’ of mining, i.e., the generation or synthesis of argumentative text. The book finishes with a summary of the argumentation mining tasks, a sketch of potential applications, and a–necessarily Jodi Schneider subjective–outlook for the field. ABOUT SYNTHESIS ThLcoinbisrca ivsroeyl uoomrfi geEi ninsa gali npprereeinrsietnengdt a vateinordsn isoC noo fom ifmp aup wtoeorr trakSn ctithe nraectse ae. pa prScehya nrastn hidne sdtihese v elSelyocntputmhreeessni stp D trooipgviiitcdasel, M O published quickly in digital and print formats. For more information, visit R our website: http://store.morganclaypool.com G A N & C L A store.morganclaypool.com Y P O O L Argumentation Mining Synthesis Lectures on Human Language Technologies Editor GraemeHirst,UniversityofToronto SynthesisLecturesonHumanLanguageTechnologiesiseditedbyGraemeHirstoftheUniversityof Toronto.Theseriesconsistsof50-to150-pagemonographsontopicsrelatingtonaturallanguage processing,computationallinguistics,informationretrieval,andspokenlanguageunderstanding. Emphasisisonimportantnewtechniques,onnewapplications,andontopicsthatcombinetwoor moreHLTsubfields. ArgumentationMining ManfredStedeandJodiSchneider 2018 QualityEstimationforMachineTranslation LuciaSpecia,CarolinaScarton,andGustavoHenriquePaetzold 2018 NaturalLanguageProcessingforSocialMedia,SecondEdition AtefehFarzindarandDianaInkpen 2017 AutomaticTextSimplification HoracioSaggion 2017 NeuralNetworkMethodsforNaturalLanguageProcessing YoavGoldberg 2017 Syntax-basedStatisticalMachineTranslation PhilipWilliams,RicoSennrich,MattPost,andPhilippKoehn 2016 Domain-SensitiveTemporalTagging JannikStrötgenandMichaelGertz 2016 iii LinkedLexicalKnowledgeBases:FoundationsandApplications IrynaGurevych,JudithEckle-Kohler,andMichaelMatuschek 2016 BayesianAnalysisinNaturalLanguageProcessing ShayCohen 2016 Metaphor:AComputationalPerspective TonyVeale,EkaterinaShutova,andBeataBeigmanKlebanov 2016 GrammaticalInferenceforComputationalLinguistics JeffreyHeinz,ColindelaHiguera,andMennovanZaanen 2015 AutomaticDetectionofVerbalDeception EileenFitzpatrick,JoanBachenko,andTommasoFornaciari 2015 NaturalLanguageProcessingforSocialMedia AtefehFarzindarandDianaInkpen 2015 SemanticSimilarityfromNaturalLanguageandOntologyAnalysis SébastienHarispe,SylvieRanwez,StefanJanaqi,andJackyMontmain 2015 LearningtoRankforInformationRetrievalandNaturalLanguageProcessing,Second Edition HangLi 2014 Ontology-BasedInterpretationofNaturalLanguage PhilippCimiano,ChristinaUnger,andJohnMcCrae 2014 AutomatedGrammaticalErrorDetectionforLanguageLearners,SecondEdition ClaudiaLeacock,MartinChodorow,MichaelGamon,andJoelTetreault 2014 WebCorpusConstruction RolandSchäferandFelixBildhauer 2013 RecognizingTextualEntailment:ModelsandApplications IdoDagan,DanRoth,MarkSammons,andFabioMassimoZanzotto 2013 iv LinguisticFundamentalsforNaturalLanguageProcessing:100Essentialsfrom MorphologyandSyntax EmilyM.Bender 2013 Semi-SupervisedLearningandDomainAdaptationinNaturalLanguageProcessing AndersSøgaard 2013 SemanticRelationsBetweenNominals ViviNastase,PreslavNakov,DiarmuidÓSéaghdha,andStanSzpakowicz 2013 ComputationalModelingofNarrative InderjeetMani 2012 NaturalLanguageProcessingforHistoricalTexts MichaelPiotrowski 2012 SentimentAnalysisandOpinionMining BingLiu 2012 DiscourseProcessing ManfredStede 2011 BitextAlignment JörgTiedemann 2011 LinguisticStructurePrediction NoahA.Smith 2011 LearningtoRankforInformationRetrievalandNaturalLanguageProcessing HangLi 2011 ComputationalModelingofHumanLanguageAcquisition AfraAlishahi 2010 IntroductiontoArabicNaturalLanguageProcessing NizarY.Habash 2010 v Cross-LanguageInformationRetrieval Jian-YunNie 2010 AutomatedGrammaticalErrorDetectionforLanguageLearners ClaudiaLeacock,MartinChodorow,MichaelGamon,andJoelTetreault 2010 Data-IntensiveTextProcessingwithMapReduce JimmyLinandChrisDyer 2010 SemanticRoleLabeling MarthaPalmer,DanielGildea,andNianwenXue 2010 SpokenDialogueSystems KristiinaJokinenandMichaelMcTear 2009 IntroductiontoChineseNaturalLanguageProcessing Kam-FaiWong,WenjieLi,RuifengXu,andZheng-shengZhang 2009 IntroductiontoLinguisticAnnotationandTextAnalytics GrahamWilcock 2009 DependencyParsing SandraKübler,RyanMcDonald,andJoakimNivre 2009 StatisticalLanguageModelsforInformationRetrieval ChengXiangZhai 2008 Copyright©2019byMorgan&Claypool Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotations inprintedreviews,withoutthepriorpermissionofthepublisher. ArgumentationMining ManfredStedeandJodiSchneider www.morganclaypool.com ISBN:9781681734590 paperback ISBN:9781681734606 ebook ISBN:9781681734613 hardcover DOI10.2200/S00883ED1V01Y201811HLT040 APublicationintheMorgan&ClaypoolPublishersseries SYNTHESISLECTURESONHUMANLANGUAGETECHNOLOGIES Lecture#40 SeriesEditor:GraemeHirst,UniversityofToronto SeriesISSN Print1947-4040 Electronic1947-4059 Argumentation Mining Manfred Stede UniversityofPotsdam Jodi Schneider UniversityofIllinoisatUrbana-Champaign SYNTHESISLECTURESONHUMANLANGUAGETECHNOLOGIES#40 M &C Morgan&cLaypool publishers ABSTRACT Argumentation mining is an application of natural language processing (NLP) that emerged a few years ago and has recently enjoyed considerable popularity, as demonstrated by a series of international workshops and by a rising number of publications at the major conferences andjournalsofthefield.Itsgoalsaretoidentifyargumentationintextordialogue;toconstruct representationsoftheconstellationofclaims,supportingandattackingmoves(indifferentlevels ofdetail);andtocharacterizethepatternsofreasoningthatappeartolicensetheargumentation. Furthermore,recentworkalsoaddressesthedifficulttasksofevaluatingthepersuasivenessand quality of arguments. Some of the linguistic genres that are being studied include legal text, studentessays,politicaldiscourseanddebate,newspapereditorials,scientificwriting,andothers. Thebookstartswithadiscussionofthelinguisticperspective,characteristicsofargumen- tativelanguage,andtheirrelationshiptocertainothernotionssuchassubjectivity. Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally very difficult toconnectthetworealmsofreasoningandtextanalysis,butweareconvincedthatitshouldbe attemptedinthelongterm,andthereforewealsotouchuponsomefundamentalsofreasoning approaches. Thenthebookturnstoitsfocus,thecomputationalsideofminingargumentationintext. Wefirstintroduceanumberofannotatedcorporathathavebeenusedintheresearch.Fromthe NLPperspective,argumentationminingsharessubtaskswithresearchfieldssuchassubjectiv- ityandsentimentanalysis,semanticrelationextraction,anddiscourseparsing.Therefore,many technicalapproachesarebeingborrowedfromthose(andother)fields.Webreakargumentation mining into a series of subtasks, starting with the preparatory steps of classifying text as argu- mentative(ornot)andsegmentingitintoelementaryunits.Then,centralstepsaretheautomatic identification of claims, and finding statements that support or oppose the claim. For certain applications, it is also of interest to compute a full structure of an argumentative constellation ofstatements. Next, we discuss a few steps that try to ‘dig deeper’: to infer the underlying reasoning pattern for a textual argument, to reconstruct unstated premises (so-called ‘enthymemes’), and to evaluate the quality of the argumentation. We also take a brief look at ‘the other side’ of mining,i.e.,thegenerationorsynthesisofargumentativetext. Thebookfinisheswithasummaryoftheargumentationminingtasks,asketchofpotential applications,anda—necessarilysubjective—outlookforthefield. KEYWORDS argumentationmining,naturallanguageprocessing,claimdetection,evidencede- tection,argumentativelanguage,argumentmodels,enthymemes
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