Article published in:
Argumentation and Meaning: Semantic and pragmatic reflexions
Edited by Steve Oswald, Sara Greco, Johanna Miecznikowski, Chiara Pollaroli and Andrea Rocci
[Journal of Argumentation in Context 9:1] 2020
► pp. 1941
References

References

Afantenos, Stergos, Andreas Peldszus, and Manfred Stede
2018 “Comparing decoding mechanisms for parsing argumentative structures”. Journal of Argumentation and Computation 9(3): 177–192. CrossrefGoogle Scholar
Al-Khatib, Khalid, Henning Wachsmuth, Johannes Kiesel, Matthias Hagen and Benno Stein
2016 “A News Editorial Corpus for Mining Argumentation Strategies”. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: 3433–3443, Osaka, Japan.Google Scholar
Bar-Haim, Roy, Lilach Edelstein, Charles Jochim, Noam Slonim
2017 "Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization". Proceedings of the 4th Workshop on Argument Mining, pages 32–38, Copenhagen, Denmark. CrossrefGoogle Scholar
Biran, Or and Owen Rambow
2011 “Identifying Justifications in Written Dialogs by Classifying Text as Argumentative.” Int. J. Semantic Computing 5 (2011): 363-381.Google Scholar
Boltuzic, Filip and Jan Snajder
2016*Fill the gap! Analyzing implicit premises between claims from online debates”. Proceedings of the Third Workshop on Argumentation Mining: 124–133, Berlin, Germany. CrossrefGoogle Scholar
Cohen, Robin
1987 “Analyzing the Structure of Argumentative Discourse”. Computational Linguistics 13(1–2):11–24.Google Scholar
Deng, Lingjia and Janyce Wiebe
2014 “Sentiment propagation via implicature constraints”. Proceedings of the 14th Conference of the European Chapter of the ACL: 377–385, Gothenburg, Sweden.Google Scholar
Eckle-Kohler, Judith, Roland Kluge, and Iryna Gurevych
2015 “On the role of discourse markers for discriminating claims and premises in argumentative discourse”. Proceedings Empirical Methods in Natural Language Processing: 2236–2242, Lisbon, Portugal.Google Scholar
Falakmasir, Mohammad Hassan, Kevin D. Ashley, Christian D. Schunn, Diane J. Litman
2014 “Identifying Thesis and Conclusion Statements in Student Essays to Scaffold Peer Review.” In: Trausan-Matu S., Boyer K.E., Crosby M., Panourgia K. (eds) Intelligent Tutoring Systems. ITS 2014. Lecture Notes in Computer Science, vol 8474. Springer, Cham. CrossrefGoogle Scholar
Fellbaum, Christiane
(ed) 1998WordNet: An Electronic Lexical Database. Cambridge, Massachusetts: MIT Press. CrossrefGoogle Scholar
Feng, Vanessa Wei and Graeme Hirst
2011 “Classifying arguments by scheme”. Proceedings Association for Computational Linguistics: Human Language Technologies: 987–996, Portland, Oregon.Google Scholar
Freeman, James B.
2011Argument Structure: Representation and Theory. Amsterdam: Springer. CrossrefGoogle Scholar
Habernal, Ivan and Iryna Gurevych
2017 “Argumentation mining in user-generated web discourse”. Computational Linguistics 43(1): 125–179. CrossrefGoogle Scholar
Hasan, Kazi Saidul and Vincent Ng
2013 “Stance classification of ideological debates: Data, models, features, and constraints”. Proceedings of the Sixth International Joint Conference on Natural Language Processing: 1348–1356, Nagoya, Japan.Google Scholar
[ p. 40 ]
Klenner, Manfred and Michael Amsler
2016 “Sentiframes: a resource for verb-centered German sentiment inference”. Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC): 2888–2891, Portoroz, Slovenia.Google Scholar
Lawrence, John and Chris Reed
2017 “Using Complex Argumentative Interactions to Reconstruct the Argumentative Structure of Large-Scale Debates”. 4th Workshop on Argumentation Mining: 108–117, Copenhagen, Denmark. CrossrefGoogle Scholar
Lippi, Marco and Paolo Torroni
2016 “Argumentation mining: State of the art and emerging trends”. ACM Transactions on Internet Technology 16(2):10:1–10:25. CrossrefGoogle Scholar
Liu, Bing
2012Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies. San Rafael (CA): Morgan and Claypool.Google Scholar
Mohammad, Saif M., Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, Colin Cherry
2016 “SemEval-2016 Task 6: Detecting Stance in Tweets”. Proceedings of SemEval 2016: 31–41, San Diego, California.Google Scholar
Palau and Moens
2009: Mochales Palau, Raquel and Marie-Francine Moens 2009 "Argumentation mining: the detection, classification and structure of arguments in text." Proceedings of the 12th international conference on artificial intelligence and law, pages 98–107, New York.Google Scholar
Peldszus, Andreas and Manfred Stede
2013 “From argument diagrams to argumentation mining in texts: A survey”. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 7(1):1–31. CrossrefGoogle Scholar
2016 “An annotated corpus of argumentative microtexts”. In Argumentation and Reasoned Action: Proceedings of 1st European Conference on Argumentation (Vol II), ed. By Dima Mohammed and Marcin Lewiński, 801–816, London: College Publications.Google Scholar
Persing, Isaac and Vincent Ng
2015 “Modeling argument strength in student essays”. Proceedings 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers): 543–552, Beijing, China.Google Scholar
Shnarch, Eyal, Ran Levy, Vikas Raykar, and Noam Slonim
2017 “Grasp: Rich patterns for argumentation mining”. Proceedings Empirical Methods in Natural Language Processing: 1356–1361, Copenhagen, Denmark.Google Scholar
Skeppstedt, Maria, Andreas Peldszus, and Manfred Stede
2018 “More or less controlled elicitation of argumentative text: enlarging a microtext corpus via crowdsourcing”. Proceedings of the 5th Workshop on Argumentation Mining: 155–163. Brussels, Belgium. CrossrefGoogle Scholar
Socher, Richard, Alex Perelygin, Jean Wu, Jason Chuang, Christopher Manning, Andrew Ng and Christopher Potts
2013 “Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank”. Proceedings of the Conference on Empirical Methods in Natural Language Processing: 1631–1642, Seattle, WA.Google Scholar
Somasundaran, Swapna and Janyce Wiebe
2010 “Recognizing stances in ideological online debates”. Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text: 116–124, Los Angeles, CA.Google Scholar
Stab, Christian and Iryna Gurevych
2014 “Annotating argument components and relations in persuasive essays”. Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: 1501–1510, Dublin, Ireland.Google Scholar
[ p. 41 ]
2017 “Parsing argumentation structures in persuasive essays”. Computational Linguistics, 43(3):619–660. CrossrefGoogle Scholar
Stede, Manfred and Jodi Schneider
2018Argumentation Mining. San Rafael (CA): Morgan and Claypool. CrossrefGoogle Scholar
Taboada, Maite, Julian Brooke, Milan Tofiloski, Kimberly Voll and Manfred Stede
2011 “Lexicon-based methods for sentiment analysis”. Computational Linguistics 37(2):267–307. CrossrefGoogle Scholar
Toulmin, Stephen
2008 “The layout of arguments”. In Reasoning: Studies of human inference and its foundations, ed. by Jonathan E. Adler and Lance J. Rips, 652–677, Cambridge: Cambridge University Press. CrossrefGoogle Scholar
Wachsmuth, Henning and Benno Stein
2017 "A Universal Model for Discourse-Level Argumentation Analysis." In: Special Section of the ACM Transactions on Internet Technology: Argumentation in Social Media, 17 (3): 28:1–28:24, June 2017 CrossrefGoogle Scholar
Wachsmuth, Henning, Martin Potthast, Khalid Al-Khatib, Yamen Ajjour, Jana Puschmann, Jiani Qu, Jonas Dorsch, Viorel Morari, Janek Bevendorff, and Benno Stein
2017a “Building an argument search engine for the web”. Proceedings of the 4th Workshop on Argumentation Mining: 49–59, Copenhagen, Denmark. CrossrefGoogle Scholar
Wachsmuth, Henning, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst, and Benno Stein
2017b “Computational Argumentation Quality Assessment in Natural Language”. Proceedings European Chapter of the Association for Computational Linguistics: 176–187, Valencia, Spain.Google Scholar
Wojatzki, Michael and Torsten Zesch
2016 “Stance-based Argument Mining – Modeling Implicit Argumentation Using Stance”. Proceedings of the German Conference on Natural Language Processing KONVENS: 313–322, Bochum, Germany.Google Scholar