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1
A Two-Level Approach to Generate Synthetic Argumentation Reports
In: ISSN: 1946-2166 ; EISSN: 1946-2174 ; Argument and Computation ; https://hal.archives-ouvertes.fr/hal-02191823 ; Argument and Computation, Taylor & Francis, 2018, 18 years of Computational Models of Natural Argument, 9 (2), pp.137-154. ⟨10.3233/AAC-180035⟩ ; https://content.iospress.com/articles/argument-and-computation/aac035 (2018)
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2
Knowledge-Driven Argument Mining Based on the Qualia Structure
In: ISSN: 1946-2166 ; EISSN: 1946-2174 ; Argument and Computation ; https://hal.archives-ouvertes.fr/hal-02535069 ; Argument and Computation, Taylor & Francis, 2017, 8 (2), pp.193-210. ⟨10.3233/AAC-170124⟩ (2017)
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3
A Two-Level Approach to Generate Synthetic Argumentation Reports
In: 17th Workshop on Computational Models of Natural Argumentation (CMNA@ICAIL 2017) ; https://hal.archives-ouvertes.fr/hal-02603764 ; 17th Workshop on Computational Models of Natural Argumentation (CMNA@ICAIL 2017), Jun 2017, Londres, United Kingdom (2017)
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4
Argument Mining: The bottleneck of knowledge and language resources
In: Proceedings of the 10th International Conference on Language Resources and Evaluation ; 10th International Conference on Language Resources and Evaluation (LREC 2016) ; https://hal.archives-ouvertes.fr/hal-01436203 ; 10th International Conference on Language Resources and Evaluation (LREC 2016), May 2016, Portoroz, Slovenia. pp. 983-990 (2016)
Abstract: International audience ; Given a controversial issue, argument mining from natural language texts (news papers, and any form of text on the Internet) is extremely challenging: domain knowledge is often required together with appropriate forms of inferences to identify arguments. This contribution explores the types of knowledge that are required and how they can be paired with reasoning schemes, language processing and language resources to accurately mine arguments. We show via corpus analysis that the Generative Lexicon, enhanced in different manners and viewed as both a lexicon and a domain knowledge representation, is a relevant approach. In this paper, corpus annotation for argument mining is first developed, then we show how the generative lexicon approach must be adapted and how it can be paired with language processing patterns to extract and specify the nature of arguments. Our approach to argument mining is thus {\bf knowledge driven
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; [INFO.INFO-LO]Computer Science [cs]/Logic in Computer Science [cs.LO]; Argument mining; Generative lexicon; Knowledge representation
URL: https://hal.archives-ouvertes.fr/hal-01436203/file/saintdizier_17222.pdf
https://hal.archives-ouvertes.fr/hal-01436203/document
https://hal.archives-ouvertes.fr/hal-01436203
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5
Knowledge-Driven Argument Mining: what we learn from corpus analysis
In: Proceedings of COMMA 2016 ; 6th International Conference on Computational Models of Argument (COMMA 2016) ; https://hal.archives-ouvertes.fr/hal-01436201 ; 6th International Conference on Computational Models of Argument (COMMA 2016), Sep 2016, Potsdam, Germany. pp. 65-72 (2016)
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6
On the quality of persuasion dialogs
In: ISSN: 0860-150X ; Studies in Logic, Grammar and Rhetoric ; https://hal.archives-ouvertes.fr/hal-03324538 ; Studies in Logic, Grammar and Rhetoric, Polish Society for Logic and Philosophy of Science, 2011, Special issue Argument and Computation, 23 (36), pp.69-98 (2011)
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7
A three-level approach to the semantics of space
In: The semantics of prepositions: from mental processing to natural language processing ; https://hal.archives-ouvertes.fr/hal-00462578 ; Cornelia Zelinsky-Wibbelt. The semantics of prepositions: from mental processing to natural language processing, Mouton de Gruyter, pp.395-439, 1993, Natural Language Processing 3 (1993)
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