Table Of ContentSeries ISSN 1947-4040
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Series Editor: Graeme Hirst,University of Toronto I
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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
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and debate, newspaper editorials, scientific writing, and others.
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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
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Besides the connection to linguistics, argumentation has for a long time been a topic in Artificial Intelligence, where the focus is
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on devising adequate representations and reasoning formalisms that capture the properties of argumentative exchange. It is generally A
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very difficult to connect the two realms of reasoning and text analysis, but we are convinced that it should be attempted in the long
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term, and therefore we also touch upon some fundamentals of reasoning approaches.
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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
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research fields such as subjectivity and sentiment analysis, semantic relation extraction, and discourse parsing. Therefore, many N
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technical approaches are being borrowed from those (and other) fields.We break argumentation mining into a series of subtasks, N
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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.
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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
