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Enabling a Bot with Understanding Argumentation and Providing Arguments

  • Boris Galitsky
Chapter

Abstract

We make our chatbot capable of exchanging arguments with users. The chatbot needs to tackle various argumentation patterns provided by a user as well as provide adequate argumentation patterns in response. To do that, the system needs to detect certain types of arguments in user utterances to “understand” her and detect arguments in textual content to reply back accordingly. Various patterns of logical and affective argumentation are detected by analyzing the discourse and communicative structure of user utterances and content to be delivered to the user. Unlike most argument-mining systems, the chatbot not only detects arguments but performs reasoning on them for the purpose of validation the claims. We explore how the chatbot can leverage discourse-level features to assess the quality and validity of arguments as well as overall text truthfulness, integrity, cohesiveness and how emotions and sentiments are communicated. Communicative discourse trees and their extensions for sentiments and noisy user generated content are employed in these tasks.

We conduct evaluation of argument detection on a variety of datasets with distinct argumentation patterns, from news articles to reviews and customer complaints, to observe how discourse analysis can support a chatbot operating in these domains. Our conclusion is that domain-independent discourse-level features are a critical source of information to enable the chatbot to reproduce such complex form of human activity as providing and analyzing arguments.

References

  1. Abbott R, Ecker B, Anand P, Walker MA (2016) Internet Argument Corp s 2.0: An SQL schema for Dialogic Social Media and the Corpora to go with it. In Language Resources and Evaluation Conference, Portorož, SloveniaGoogle Scholar
  2. Ajjour Y, Chen WF, Kiesel J, Wachsmuth H, Stein B (2017) Unit segmentation of argumentative texts. In: Proceedings of the 4th workshop on argument mining. University of Duisburg-Essen, Copenhagen, pp 118–128Google Scholar
  3. Aker A, Sliwa A, Ma Y, Liu R, Borad N, Ziyaei SF, Ghbadi M (2017) What works and what does not: classifier and feature analysis for argument mining. In: Proceedings of the 4th workshop on argument mining. University of Duisburg-Essen, Copenhagen, pp 91–96Google Scholar
  4. Alsinet T, Chesñevar CI, Godo L, Simari GR (2008) A logic programming framework for possibilistic argumentation: formalization and logical properties. Fuzzy Sets Syst 159(10):1208–1228MathSciNetzbMATHGoogle Scholar
  5. Amgoud L, Besnard P, Hunter A (2015) Representing and reasoning about arguments mined from texts and dialogues. In: ECSQARU, pp 60–71Google Scholar
  6. Bar-Haim R, Edelstein L, Jochim C, Slonim N (2017) Improving claim stance classification with lexical knowledge expansion and context utilization. In: Proceedings of the 4th workshop on argument mining. University of Duisburg-Essen, Copenhagen, pp 32–38Google Scholar
  7. Baroni P, Giacomin M (2002) Argumentation through a distributed self-stabilizing approach. J Exp Theor Artif Intell 14(4):273–301zbMATHGoogle Scholar
  8. Barzilay R, Lapata M (2008) Modeling local coherence: an entity-based approach. Comput Linguist 34:1, 1–1,34Google Scholar
  9. BBC (2005) Suicide bomber trial: emails in full. Assessed 11–28-05 at news.bbc.co.uk/1/hi/uk/ 3825765.stm
  10. BBC (2018) Trump Russia affair: key questions answered. http://www.bbc.com/news/world-us-canada-42493918, Last downloaded May 1, 2018
  11. Bedi P, Vashisth P (2015) Argumentation-enabled interest-based personalised recommender system. J Exp Theor Artif Intell 27(2):199–226Google Scholar
  12. Bentahar J, Moulin B, Bélanger M (2010) A taxonomy of argumentation models used for knowledge representation. Artif Intell Rev 33:211–259Google Scholar
  13. Berzlánovich I, Egg M, Redeker G (2008) Coherence structure and lexical cohesion in expository and persuasive texts. In: Benz A, Kühnlein P, Stede M (eds) Proceedings of the workshop on constraints in discourse III. University of Potsdam, PotsdamGoogle Scholar
  14. Biran O, Rambow O (2011) Identifying justifications in written dialogs by classifying text as argumentative. Int J Semant Computing 05(04):363–381zbMATHGoogle Scholar
  15. Boguslavsky I, Iomdin L, Sizov V (2004) Multilinguality in ETAP-3: reuse of lexical resources. In: Sérasset G, Armstrong S, Boitet C, Popescu-Belis A, Tufis D (eds) Proceedings of the workshop on multilingual linguistic Ressources (MLR ‘04). Association for Computational Linguistics, Stroudsburg, pp 7–14Google Scholar
  16. Bondarenko A, Dung P, Kowalski R, Toni F (1997) An abstract, argumentation-theoretic approach to default reasoning. Artif Intell 93:63–101MathSciNetzbMATHGoogle Scholar
  17. Britt MA, Larson AA (2003) Constructing representations of arguments. J Mem Lang 48(4):794–810Google Scholar
  18. Cabrio E, Villata S (2012) Combining textual entailment and argumentation theory for supporting online debates interactions. ACL 2:208–212Google Scholar
  19. Carlson L, Marcu D, Okurowski ME (2001) Building a discourse-tagged corpus in the framework of rhetorical structure theory. In: Proceedings of the second SIGdial workshop on discourse and dialogue, pp 1–10Google Scholar
  20. Carreyrou J (2016) Hot startup theranos has struggled with its blood-test technology. http://www.wsj.com/articles/theranos-has-struggled-with-blood-tests-1444881901#livefyre-comment
  21. Charolles M (1995) Cohesion, coherence et pertinence de discours. Travaux de Linguistique 29:125–151Google Scholar
  22. Constantinos JS, Sarmaniotis C, Stafyla A (2003) CRM and customer-centric knowledge management: an empirical research. Bus Process Manag J 9(5):617–634Google Scholar
  23. Cristea D (1998) Formal proofs in Incremental Discourse Processing and Veins Theory, Research Report TR98 – Dept. of Computer Science. University “A.I.Cuza”, IaşiGoogle Scholar
  24. Damer TE (2009) Attacking faulty reas ning: a practical guide to fallacy-free reasoning. Wadsworth Cengage LearningGoogle Scholar
  25. Das D, Chen D, Martins AFT, Schneider N, Smith NA (2014) Frame-semantic parsing. Comput Linguist 40(1):9–56Google Scholar
  26. DeVillez R (2003) Writing: step by step. Kendall Hunt, DubuqueGoogle Scholar
  27. Eckle-Kohler, J Kluge R, Gurevych I (2015) On the role of discourse markers for discriminating claims and premises in argumentative discourse. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language ProcessingGoogle Scholar
  28. Egg M, Redeker G (2008) Underspecified discourse representation. In: Benz A, Kühnlein P (eds) Constraints in discourse. Benjamins, Amsterdam, pp 117–138Google Scholar
  29. Feng, V.W. and Hirst, G. (2011) Classifying arguments by scheme. In Proceedings of the 49th annual meeting of the Association for Computational Linguistics, Portland, OR, pp 987–996Google Scholar
  30. Feng, V.W. and Graeme Hirst (2012) Text-level discourse parsing with rich linguistic features. In Proceedings of the 50th annual meeting of the association for computational linguistics: human language technologies (ACL 2012), pp 60–68, Jeju, KoreaGoogle Scholar
  31. Feng VW, Hirst G (2014) A linear-time bottom-up discourse parser with constraints and post-editing. In: Proceedings of the 52nd annual meeting of the Association for Computational Linguistics. ACL, BaltimoreGoogle Scholar
  32. Ferretti E, Errecalde ML, García AJ, Simari GR (2014) A possibilistic defeasible logic programming approach to argumentation-based decision-making. J Exp Theor Artif Intell 26(4):519–550Google Scholar
  33. Florou E, Konstantopoulos S, Koukourikos A, Karampiperis P (2013) Argument extraction for supporting public policy formulation. In Proceedings of the 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. ACL, pp 49–54Google Scholar
  34. Foltz PW, Kintsch W, Landauer TK (1998) The measurement of textual coherence with latent semantic analysis. Discour Process 25:285–307Google Scholar
  35. Freeley AJ, Steinberg DL (2008) Argumentation and debate. Cengage, WadsworthGoogle Scholar
  36. Galitsky B (2012) Machine learning of syntactic parse trees for search and classification of text. Eng Appl AI 26(3):1072–1091Google Scholar
  37. Galitsky B (2015) Detecting rumor and disinformation by web mining, AAAI spring symposium series, pp 16–23Google Scholar
  38. Galitsky B (2017) Improving relevance in a content pipeline via syntactic generalization. Eng Appl Artif Intell 58:1–26Google Scholar
  39. Galitsky B (2018) Enabling chatbots by detecting and supporting argumentation. US Patent App. 16/010,091Google Scholar
  40. Galitsky B, de la Rosa JL (2011) Concept-based learning of human behavior for customer relationship management. Inf Sci 181(10):2016–2035Google Scholar
  41. Galitsky B, Kuznetsov SO (2008) Learning communicative actions of conflicting human agents. J Exp Theor Artif Intell 20(4):277–317zbMATHGoogle Scholar
  42. Galitsky B, Parnis A (2018) Accessing validity of argumentation of agents of the internet of everything. In: Lawless WF, Mittu R, Sofge D, Russell S (ed) Artificial Intelligence for the Internet of Everything (to appear)Google Scholar
  43. Galitsky B and Taylor J (2018) Discovering and assessing heated arguments at the discourse level. Computational linguistics and intellectual technologies: proceedings of the international conference “Dialogue 2018”. Moscow, May 30–June 2Google Scholar
  44. Galitsky B, González MP, Chesñevar CI (2009) A novel approach for classifying customer complaints through graphs similarities in argumentative dialogues. Decis Support Syst 46(3):717–729Google Scholar
  45. Galitsky B, de la Rosa J-L, Kovalerchuk B (2011) Discovering common outcomes of agents’ communicative actions in various domains. Knowl -Based Syst 24(2):210–229Google Scholar
  46. Galitsky B, Ilvovsky D, Kuznetsov SO, Strok F (2013) Matching sets of parse trees for answering multi-sentence questions // Proceedings of the Recent Advances in Natural Language Processing, RANLP 2013. – INCOMA Ltd., Shoumen, Bulgaria, pp 285–294Google Scholar
  47. Galitsky B, Ilvovsky D, Kuznetsov SO (2015) Text Classification into Abstract Classes Based on Discourse Structure, in: Proceedings of the Recent Advances in Natural Language Processing, RANLP 2015. pp 201–207Google Scholar
  48. Galitsky B, Ilvovsky D, Kuznetsov SO (2018) Detecting logical argumentation in text via communicative discourse tree. J Exp Theor Artif Intell 30(5):1–27Google Scholar
  49. Garcia A, Simari GR (2004) Defeasible logic programming: an argumentative approach. Theory and Practice of Logic Programming 4(1–2):95–138MathSciNetzbMATHGoogle Scholar
  50. Ghosh D, Muresan S, Wacholder N, Aakhus M, Mitsui M (2014) Analyzing argumentative discourse units in online interactions. In: Proceedings of the first workshop on argumentation mining. ACL, Baltimore, pp 39–48Google Scholar
  51. Golightly KB, Sanders G (2000) Writing and reading in the disciplines. Pearson Custom Publishing, Upper Saddle RiverGoogle Scholar
  52. Goutsos D (1997) Modeling discourse topic: sequential relations and strategies in expository text. Ablex, NorwoodGoogle Scholar
  53. Grosz BJ, Sidner CL (1986) Attention, intentions, and the structure of discourse. Comput Linguist 12(3):175–204Google Scholar
  54. Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1):10–18Google Scholar
  55. Halliday MAK, Hasan R (1976) Cohesion in English. Longman, LondonGoogle Scholar
  56. Hobbs J (1979) Coherence and Coreference. Cogn Sci 3(1):67–90Google Scholar
  57. Hogenboom A, Frasincar F, de Jong F, Kaymak U (2015a) Using rhetorical structure in sentiment analysis. Commun ACM 58:69–77Google Scholar
  58. Hogenboom A, Frasincar F, de Jong F, Kaymak U (2015b) Polarity classification using structure-based vector representations of text. Decis Support Syst 74:46–56Google Scholar
  59. Houngbo H, Mercer R (2014) An automated method to build a corpus of rhetorically-classified sentences in biomedical texts. Proceedings of the First Workshop on Argumentation Mining. Baltimore, Maryland USA, June 26, 2014 Association for Computational Linguistics, pp 19–23Google Scholar
  60. Ilvovsky, D. 2014. Going beyond sentences when applying tree kernels. Proceedings of the student research workshop. ACL pp 56–63Google Scholar
  61. Iruskieta M, da Cunha I, Taboada M (2014) A qualitative comparison method for rhetorical structures: identifying different discourse structures in multilingual corpora. Lang Resour Eval 49(2):263–309Google Scholar
  62. Jørgensen AK, Hovy D, Søgaard A (2015) Proceedings of the ACL 2015 Workshop on Noisy User-generated Text, pp 9–18Google Scholar
  63. Joty S, Moschitti A (2014) Discriminative reranking of discourse parses using tree kernels. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)Google Scholar
  64. Jindal N, Liu B (2008) Opinion spam and analysis. Proceedings of International Conference on Web Search and Data Mining WSDM-2008Google Scholar
  65. Joty S, Carenini G, Ng RT, Mehdad Y (2013) Combining intra-and multi- sentential rhetorical parsing for document-level dis- course analysis. ACL 1:486–496Google Scholar
  66. Joty S, Carenini G, Ng RT (2015) CODRA: a novel discriminative framework for rhetorical analysis. Comput Linguist 41(3):385–435MathSciNetGoogle Scholar
  67. Kent I, Nicholls W (1977) The psychodynamics of terrorism. Mental Health & Society 4(1-sup-2):1–8Google Scholar
  68. Kipper K, Korhonen A, Ryant N, Palmer M (2008) A large-scale classification of English verbs. Lang Resour Eval J 42:21–40Google Scholar
  69. Kirschner, C., Eckle-Kohler J, Gurevych I (2015) Linking the thoughts: analysis of argumentation structures in Scientific Publications NAACL HLT 2015 2nd Workshop on Argumentation MiningGoogle Scholar
  70. Kleiber G (1994) Anaphores et pronoms. Louvain-la-Neuve, DuculotGoogle Scholar
  71. Kong KCC (1998) Are simple business request letters really simple? A comparison of Chinese and English business request letters. Text 18(1):103–141MathSciNetGoogle Scholar
  72. Kwon N, Liang Z, Hovy E, Shulman SW (2007) Identifying and classifying subjective claims. In Proceedings of the 8th Annual International Conference on Digital Government Research: Bridging Disciplines & Domains. Philadelphia, PA, USA, pp 76–81Google Scholar
  73. Landlord vs Tenant (2018.) www.landlordvtenant.com. Last downloaded August 20, 2018
  74. Lawrence J, Reed C (2015) Combining argument mining techniques, NAACL HLT 2015 2nd Workshop on Argumentation MiningGoogle Scholar
  75. Lawrence J, Reed C (2017) Mining argumentative structure from natural language text using automatically generated premise-conclusion topic models. Proceedings of the 4th Workshop on Argument Mining, pp 39–48Google Scholar
  76. Lazaridou A, Titov I, Sporleder C (2013) A Bayesian model for joint unsupervised induction of sentiment, aspect and discourse representations. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pp 1630–1639, Sofia, Bulgaria, August 4–9Google Scholar
  77. Lee D (2001) Genres, registers, text types, domains and styles: clarifying the concepts and navigating a path through the BNC jungle. Lang Learn Technol 5(3):37–72Google Scholar
  78. Lin Z, Ng HT, Kan M-Y (2014) A PDTB-styled end-to-end discourse parser. Nat Lang Eng 20(2):151–184Google Scholar
  79. MacEwan EJ (1898) The essentials of argumentation. D. C. Heath, BostonGoogle Scholar
  80. Makhalova T, Ilvovsky D, Galitsky B (2015) Pattern structures for news clustering. In Proceedings of the 4th International Conference on What can FCA do for Artificial Intelligence? –. CEUR-WS.org, Aachen, Germany, Germany, pp 35–42
  81. Mann W, Matthiessen C, Thompson S (1992) Rhetorical structure theory and text analysis. In: Mann WC, Thompson SA (eds) Discourse description: diverse linguistic analyses of a fund-raising text. Amsterdam, pp 39–78Google Scholar
  82. Marcu D (2000) The theory and practice of discourse parsing and summarization. MIT press, Cambridge MAzbMATHGoogle Scholar
  83. Markle-Huß J, Feuerriegel S, Prendinger H (2017) Improving sentiment analysis with document-level semantic relationships from rhetoric discourse structures, 50th Hawaii International Conference on System SciencesGoogle Scholar
  84. McNamara DS, Kintsch E, Songer NB, Kintsch W (1996) Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cogn Instr 14(1):1–43Google Scholar
  85. Mercier H, Sperber D (2011) Why do humans reason. Arguments for an argumentative theory. Behav Brain Sci 34(2):57–111Google Scholar
  86. Micheli R (2008, October) Emotions as objects of argumentative constructions. Argumentation 24(1):1–17MathSciNetGoogle Scholar
  87. Mitocariu E, Alexandru D, Cristea D (2013) Comparing discourse tree structures. Computational linguistics and intelligent text processing: 14th International Conference, CICLing 2013, Samos, Greece, March 24–30, 2013, Proceedings, Part IGoogle Scholar
  88. Mochales R, Moens M-F (2011, April) Argumentation mining. Artificial Intelligence and Law 19(1):1–22Google Scholar
  89. Moens MF, Boiy E, Palau RM, Reed C (2007) Automatic detection of arguments in legal texts. In Proceedings of the 11th International Conference on Artificial Intelligence and Law, ICAIL ‘07, Stanford, CA, USA, pp 225–230Google Scholar
  90. O’reilly T, McNamara DS (2007) Reversing the reverse cohesion effect: good texts can be better for strategic, high-knowledge readers. Discourse Process 43(2):121–152Google Scholar
  91. Oatley K, Jenkins JM (1996) Understanding emotions. Wiley, HobokenGoogle Scholar
  92. Oraby S, Reed L, Compton R, Riloff E, Walker M, Whittaker S (2015) And that’s a fact: distinguishing factual and emotional argumentation in online dialogue. In: The 2nd Workshop on Argumentation Mining, at The North American Chapter of the Association for Computational Linguistics (NAACL), Denver, ColoradoGoogle Scholar
  93. Ott M, Choi Y, Cardie C, Hancock JT (2011) Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language TechnologiesGoogle Scholar
  94. Ott M, Cardie C, Hancock JT (2013) Negative deceptive opinion spam. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language TechnologiesGoogle Scholar
  95. Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Barcelona, Spain — July 21–26Google Scholar
  96. Peldszus A, Stede M (2013) From argument diagrams to argumentation mining in texts: a survey. Int J Cognit Inf Nat Intell 7(1):1–31Google Scholar
  97. Pelsmaekers K, Braecke C, Geluykens R (1998) Rhetorical relations and subordination in L2 writing. In: Sánchez-Macarro A, Carter R (eds) Linguistic choice across genres: variation in spoken and written English. John Benjamins, Amsterdam/Philadelphia, pp 191–213Google Scholar
  98. Pendyala VS, Figueira S (2015) Towards a truthful world wide web from a humanitarian perspective. Global Humanitarian Technology Conference (GHTC), 2015 IEEE, Issue Date: 8–11 Oct. 2015Google Scholar
  99. Persing I, Ng V (2015) Modeling argument strength in student essays. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), ACL ‘15, Beijing, China, pp 543–552Google Scholar
  100. Pisarevskaya D, Litvinova T, Litvinova O (2017) Deception detection for the Russian language: lexical and syntactic parameters. Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval / RANLPGoogle Scholar
  101. Prasad R, Dinesh N, Lee A, Miltsakaki E, Robaldo L, Joshi A, Webber B (2008) The Penn discourse TreeBank 2.0. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), pp 28–30Google Scholar
  102. Redeker G (2000) Coherence and structure in text and discourse. In: Black W, Bunt H (eds) Abduction, belief and context in dialogue. Studies in computational pragmatics. Benjamins, Amsterdam, pp 233–263Google Scholar
  103. Rooney N, Wang H and Browne F (2012) Applying kernel methods to argumentation mining. In Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society Conference Applying, pp 272–275Google Scholar
  104. Rouhana N, Bar-Tal D (1998) Psychological dynamics of intractable ethnonational conflicts: the Israeli-Palestinian case. Am Psychol 53:761–770Google Scholar
  105. Sardianos C, Katakis IM, Petasis G, Karkaletsis V (2015) Argument extraction from news. In Proceedings of the 2nd Workshop on Argumentation Mining, Denver, CO, USA, pp 56–66Google Scholar
  106. Scheffler T, Stede M (2016) Mapping PDTB-style connective annotation to RST-style discourse annotation. In Proceedings of the 13th Conference on Natural Language Processing (KONVENS 2016)Google Scholar
  107. Schnedecker C (2005) Les chaînes de reference dans les portraits journalistiques: éléments de description. Travaux de Linguistique 2:85–133Google Scholar
  108. Scholman MCJ, Demberg V (2017) Examples and specifications that prove a point: identifying elaborative and argumentative discourse relations. Dialogue Discourse 8(2):56–83Google Scholar
  109. Searle J (1969) Speech acts: an essay in the philosophy of language. Cambridge University Press/Series ACM, Cambridge/New York, pp 19–33Google Scholar
  110. Severyn A, Moschitti A (2012) Fast support vector machines for convolution tree kernels. Data Mining Knowledge Discovery 25.– 2012, pp 325–357Google Scholar
  111. Socher R, Perelygin A, Wu J, Chuang J, Manning C, Ng A, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. Conference on Empirical Methods in Natural Language Processing (EMNLP 2013)Google Scholar
  112. Somasundaran S, Wiebe J (2009) Recognizing stances in online debates. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP. Suntec, Singapore, pp 226–234Google Scholar
  113. Stab C, Gurevych I (2014) Identifying argumentative discourse structures in persuasive essays. In: Proceedings of the 2014 conference on empirical methods in natural language processing, EMNLP ‘14. Doha, Qatar, pp 46–56Google Scholar
  114. Stab C, Gurevych I (2016) Recognizing the absence of opposing arguments in persuasive essays. ACL 2016Google Scholar
  115. Stab C, Gurevych I (2017) Recognizing insufficiently supported arguments in argumentative essaysGoogle Scholar
  116. Surdeanu M, Hicks T, Valenzuela-Escarcega MA (2015) Two practical rhetorical structure theory parsers. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies: Software Demonstrations (NAACL HLT)Google Scholar
  117. Taboada M (2004) The genre structure of bulletin board messages. Text Technol 13(2):55–82Google Scholar
  118. Torrance M, Bouayad-Agha N (2001) Rhetorical structure analysis as a method for understanding writing processes. In: Degand L, Bestgen Y, Spooren W, van Waes L (eds) Multidisciplinary approaches to discourse. Nodus, AmsterdamGoogle Scholar
  119. Tweety (2016) https://javalibs.com/artifact/net.sf.tweety.arg/delp. Last downloaded Dec 12, 2018
  120. van der Wees M, Bisazza A, Monz C (2015) Five shades of noise: analyzing machine translation errors in user- generated text. Proceedings of the ACL 2015 Workshop on Noisy User-generated TextGoogle Scholar
  121. Van Dijk T (1977) Text and context. Explorations in the semantics and pragmatics of discourse. Longman, LondonGoogle Scholar
  122. Van Eemeren FH, Grootendorst R, Henkemans FS (1996) Fundamentals of argumentation theory: a handbook of historical backgrounds and contemporary developments. Routledge, Taylor & Francis Group, LondonGoogle Scholar
  123. Virtanen T (1995) Analysing argumentative strategies: a reply to a complaint. Angl Turkuensia 14:539–547Google Scholar
  124. Walton D (1996) Argumentation schemes for presumptive reasoning. Routledge, New YorkGoogle Scholar
  125. Walton D, Reed C, Macagno F (2008) Argumentation Schemes. Cambridge University Press, CambridgezbMATHGoogle Scholar
  126. Wang W, Su J, Tan CL (2010) Kernel based discourse relation recognition with temporal ordering information. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp 710–719Google Scholar
  127. Webber B, Egg M, Kordoni V (2012) Discourse structure and language technology. Nat Lang Eng 18:437–490Google Scholar

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Authors and Affiliations

  • Boris Galitsky
    • 1
  1. 1.Oracle (United States)San JoseUSA

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