[1] 赫尔利帕特里克. 简明逻辑学导论[M]. 陈波, 宋文淦, 译. 10版. 北京:世界图书出版公司, 2010:5-7.
[2] WALLTON D. Argumentation theory:a very short introduction[G]//Argumentation in artificial intelligence. Boston:Springer, 2009:1-22.
[3] MOENS M F, BOIY E, PALAU R M, et al. Automatic detection of arguments in legal texts[C]//Proceedings of the 11th international conference on artificial intelligence and law-ICAIL. Stanford:ACM Press, 2007:225-230.
[4] KWON N, HOVY E, ZHOU L, et al. Identifying and classifying subjective claims[C]//Proceedings of the 8th annual international conference on digital government research. CA:ACM Press, 2007:76-81.
[5] PALAU R M, MOENS M F. Argumentation mining:The detection, classification and structure of arguments in text[C]//Proceedings of the 12th international conference on artificial intelligence and law. New York:ACM, 2009:98-107.
[6] First workshop on argumentation mining[EB/OL].[2020-03-07]. https://www.uncg.edu/cmp/ArgMining2014/.
[7] SICSA Workshop on argument mining 2014[EB/OL],[2020-03-07]. http://www.arg-tech.org/index.php/sicsa-workshop-on-argument-mining-2014/.
[8] MOCHALES P R, MOENS M F. Study on sentence relations in the automatic detection of argumentation in legal cases[C]//Proceedings of the 2007 conference on legal knowledge and information systems. The Netherlands:IOS Press, 2007:89-98.
[9] SONG Y, HEILMAN M, BEIGMAN B, et al. Applying argumentation schemes for essay scoring[C]//Proceedings of the first workshop on argumentation mining. Baltimore:Association for Computational Linguistics, 2014:69-78.
[10] 熊才权, 孙贤斌, 欧阳勇. 辩论的逻辑模型研究综述[J]. 模式识别与人工智能, 2010, 23(3):362-368.
[11] BENTAHAR J, MOULIN B, BELANGER M. A taxonomy of argumentation models used for knowledge representation[J]. Artificial intelligence review, 2010, 33(3):211-259.
[12] TOULMIN S E. The Uses of Argument[M]. London:Cambridge University Press, 1958.
[13] DUNG P M. On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and N-person games[J]. Artificial intelligence, 1995, 77(2):321-357.
[14] PERELMAN C, OLBRECHTS-TYTECA L. The New Rhetoric:A treatise on argumentation[M]. WILKINSON J, WEAVER P, trans. Notre Dame:University of Notre Dame Press, 1971.
[15] FREEMAN J B. Argument structure:Representation and theory[M]. Netherlands:Springer, 2011:3-6.
[16] THOMAS S N. Practical reasoning in natural language[M]. New Jeresy:Prentice-Hall, 1986.
[17] 王建芳. 基于论辩的论证结构研究——弗里曼模型与图尔敏模型的比较[J]. 逻辑学研究, 2016, 9(3):42-56.
[18] 张斌峰, 侯郭垒. 论证型式的特征及其功能[J]. 湖北大学学报(哲学社会科学版), 2018, 45(6):66-72,176.
[19] WALTON W. Argumentation schemes[M]. London:Cambridge University Press, 2008.
[20] LAWRENCE J, REED C. Argument mining using argumentation scheme structures[J]. Frontiers in artificial intelligence and applications, 2016, 287:379-390.
[21] STAB C. Argumentative writing support by means of natural language processing[D]. Darmstadt:der Technischen Universität Darmstadt, 2017.
[22] LAUSCHER A, GLAVAS G, PONZETTO S P. An argument-annotated corpus of scientific publications[C]//Proceedings of the 5th workshop on argument mining. Brussels:Association for Computational Linguistics, 2018:40-46.
[23] KIRSCHNER C, ECKLE-KOHLER J, GUREVYCH I. Linking the thoughts:analysis of argumentation structures in scientific publications[C]//Proceedings of the 2nd workshop on argumentation mining. Denver, CO:Association for Computational Linguistics, 2015:1-11.
[24] PELDSZUS A. Automatic recognition of argumentation structure in short monological texts[D]. Potsdam:University of Potsdam, 2017.
[25] STAB C, KIRSCHNER C, ECKLE K J, et al. Argumentation mining in persuasive essays and scientific articles from the discourse structure perspective[C]//Proceedings of the workshop on frontiers and connections between argumentation theory and natural language. Bertinoro, Italy:2014:1-10.
[26] PASSON M, LIPPI M, SERRA G, et al. Predicting the usefulness of amazon reviews using off-the-shelf argumentation mining[C]//Proceedings of the 5th workshop on argument mining. Brussels:ACL, 2018:35-39.
[27] BOLTU?IC F, ŠNAJDER J. Fill the gap! Analyzing implicit premises between claims from online debates[C]//Proceedings of the third workshop on argument mining. Berlin:ACL, 2016:124-133.
[28] GHOSH D, MURESAN S, WACHOLDER N, et al. Analyzing argumentative discourse units in online interactions[C]//Proceedings of the First Workshop on argumentation mining. Baltimore:ACL, 2014:39-48.
[29] BOLTU?IC F, ŠNAJDER J. Back up your stance:Recognizing arguments in online discussions[C]//Proceedings of the first workshop on argumentation mining. Baltimore:ACL, 2014:49-58.
[30] MORIO G, FUJITA K. Annotating online civic discussion threads for argument mining[C]//International conference on Web intelligence 2018. Marca Chile:IEEE:546-553.
[31] HIDEY C, MUSI E, HWANG A, et al. Analyzing the semantic types of claims and premises in an online persuasive forum[C]//Proceedings of the 4th workshop on argument mining. Copenhagen:ACL, 2017:11-21.
[32] ACCUOSTO P, SAGGION H. Discourse-driven argument mining in scientific abstracts[C]//Natural language processing and information systems. Cham:Springer, 2019, 182-194.
[33] LIPPI M, TORRONI P. Argumentation mining:state of the art and emerging trends[J]. ACM Transactions on internet technology, 2016, 16(2):1-25.
[34] GOUDAS T, LOUIZOS C, PETASIS G, et al. Argument extraction from news, blogs, and social media[C]//Artificial intelligence:methods and applications. Greece:Springer, 2014:287-299.
[35] FLOROU E, KONSTANTOPOULS S, Koukourikos A, et al. Argument extraction for supporting public policy formulation[C]//Proceedings of the 7th workshop on language technology for cultural heritage, social sciences, and humanities. Sofia:ACL, 2013:49-54.
[36] DUSMANU M, CABRIO E, VILLATA S. Argument mining on twitter:arguments, facts and sources[C]//Proceedings of the 2017 conference on empirical methods in natural language processing. Copenhagen:ACL, 2017:2317-2322.
[37] REIMERS N, SCHILLER B, BECK T, et al. Classification and clustering of arguments with contextualized word embeddings[C]//Proceedings of the 57th annual meeting of the association for computational linguistics. Florence:ACL, 2019:567-578.
[38] FERRARA A, MONTANELLI S, PETASIS G. Unsupervised detection of argumentative units though topic modeling techniques[C]//Proceedings of the 4th workshop on argument mining. Copenhagen:ACL, 2017:97-107.
[39] LAWRENCE J, REED C, ALLEN C, et al. Mining arguments From 19th century philosophical texts using topic based modelling[C]//Proceedings of the first workshop on argumentation mining. Maryland:ACL, 2014:79-87.
[40] SARDIANOS C, KATAKIS I M, PETASIS G, et al. Argument extraction from news[C]//Proceedings of the 2nd workshop on argumentation mining. Denver:ACL, 2015:56-66.
[41] LI M, GAO Y, WEN H, et al. Joint RNN model for argument component boundary detection[EB/OL].[2020-03-07]. http://arxiv.org/abs/1705.02131.
[42] PETASIS G. segmentation of argumentative texts with contextualized word representations[C]//Proceedings of the 6th workshop on argument mining. Florence:ACL, 2019:1-10.
[43] TRAUTMANN D, DAXENBERGER J, STAB C, et al. Fine-Grained argument unit recognition and classification[C]//The thirty-fourth AAAI conference on artificial intelligence (AAAI 2020). New York:AAAI, 2020:9048-9056.
[44] LIPPI M, TORRONI P. Context-independent claim detection for argument mining[C]//Proceedings of the 24th international joint conference on artificial intelligence. Argentina:AAAI Press, 2015:185-191.
[45] ECKLE-KOHLER J, KLUGE R, GUREVYCH I. On the role of discourse markers for discriminating claims and premises in argumentative discourse[C]//Proceedings of the 2015 conference on empirical methods in natural language processing. Lisbon:ACL, 2015:2236-2242.
[46] STAB C, GUREVYCH I. Identifying argumentative discourse structures in persuasive esays[C]//Proceedings of the 2014 Conference on empirical methods in natural language processing. Doha:ACL, 2014:46-56.
[47] LUGINI L, LITMAN D. Argument component classification for classroom discussions[C]//Proceedings of the 5th workshop on argument mining. Brussels:ACL, 2018:57-67.
[48] DUTTA S, DAS D, CHAKRABORTY T. Changing views:Persuasion modeling and argument extraction from online discussions[J]. Information processing & management, 2019, 57(2):1-14.
[49] GARCLA-GORROSTIETA J M, LOPEZ-LOPEZ A. Argument component classification in academic writings[J]. Journal of intelligent & fuzzy systems, 208, 34(5):3037-3047.
[50] STAB C, GUREVYCH I. Parsing argumentation structures in persuasive essays[J]. Computational linguistics, 2017, 43(3):619-659.
[51] AKER A, SLIWA A, MA Y, et al. What works and what was not:Classifier and feature analysis for argument mining[C]//Proceedings of the 4th workshop on argument mining. Copenhagen:ACL, 2017:91-96.
[52] LAWRENCE J, REED C. Combining argument mining techniques[C]//Proceedings of the 2nd workshop on argumentation mining. Denver:ACL, 2015:127-136.
[53] DAGAN I, DOLAN B, MAGNINIL B, et al. Recognizing textual entailment:rational, evaluation and approaches[J]. Journal of Natural Language Engineering, 2009, 15(4), 1-17.
[54] CABRIO E, VILLATA S. Combining textual entailment and argumentation theory for supporting online debates interactions[C]//Proceedings of the 50th annual meeting of the association for computational linguistics. Korea:ACL, 2012:208-212.
[55] HOU Y, JOCHIM C. Argument relation classification using a joint inference model[C]//Proceedings of the 4th workshop on argument mining. Copenhagen:ACL, 2017:60-66.
[56] GALASSI A, LIPPI M, TORRONI P. Argumentative link prediction using residual networks and multi-objective learning[C]//Proceedings of the 5th workshop on argument mining. Brussels:ACL, 2018:1-10.
[57] NGUYEN H, LITMAN D. Context-aware argumentative relation mining[C]//Proceedings of the 54th annual meeting of the association for computational linguistics. Berlin:ACL, 2016:1127-1137.
[58] MITROVIC J, O'REILLY C, MLADENOVIC M, et al. Ontological representations of rhetorical figures for argument mining[J]. Argument & computation, 2017, 8(3):267-287.
[59] GREEN N L. Representation of argumentation in text with rhetorical structure theory[J]. Argumentation, 2010, 24(2):181-196.
[60] RAJENDRAN P, BOLLEGALA D, PARSONS S. Contextual stance classification of opinions:a step towards enthymeme reconstruction in online reviews[C]//Proceedings of the third workshop on argument mining. Berlin:ACL, 2016:31-39.
[61] GREEN N. Manual identification of arguments with implicit conclusions using semantic rules for Argument Mining[C]//Proceedings of the 4th workshop on argument mining. Copenhagen:ACL, 2017:73-78.
[62] GREEN N. Towards mining scientific discourse using argumentation schemes[J]. Argument & computation, 2018, 9(2):121-135.
[63] NIVEN T, KAO H-Y. Probing neural network comprehension of natural language arguments[C]//Proceedings of the 57th annual meeting of the association for computational linguistics. Florence:ACL, 2019:4658-4664.
[64] PELDSZUS A, STEDE M. Joint prediction in MST-style discourse parsing for argumentation mining[C]//Proceedings of the 2015 conference on empirical methods in natural language processing. Lisbon:ACL, 2015:938-948.
[65] AFANTENOS S, PELDSZUS A, STEDE M. Comparing decoding mechanisms for parsing argumentative structures[J]. Argument & computation, 2018, 9(3):177-192.
[66] WALKER V R, FOERSTER D, PONCE J M, et al. Evidence types, credibility factors, and patterns or soft rules for weighing conflicting evidence:argument mining in the context of legal rules governing evidence assessment[C]//Proceedings of the 5th workshop on argument mining. Belgium:ACL, 2018:68-78.
[67] AL-ABDULAKRIM L, ATKINSON K, BENCH-CAPON T. Statement types in legal argument[C]//Frontiers in artificial intelligence and applications. Hague:IOS Press, 2016:3-12.
[68] PERSING I, NG. Modeling argument strength in student essays[C]//Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing. Beijing:ACL, 2015:543-552.
[69] WACHSMUTH H, NADERI N, HABERNAL I, et al. Argumentation quality assessment:Theory vs. Practice[C]//Proceedings of the 55th annual meeting of the association for computational linguistics. Vancouver:ACL, 2017:250-255.
[70] WACHSMUTH H, AL KHATIB K, STEIN B. Using argument mining to assess the argumentation quality of essays[C]//Proceedings of COLING 2016, the 26th international conference on computational linguistics:technical papers. Japan:The COLING 2016 Organizing Committee, 2016:1680-1691.
[71] PERSING I, NG V. Why can't you convince me? Modeling weaknesses in unpersuasive arguments[C]//Proceedings of the twenty-sixth international joint conference on artificial intelligence. Melbourne:International Joint Conferences on Artificial Intelligence Organization, 2017:4082-4088.
[72] KONAT B, LAWRENCE J, PARK J, et al. A corpus of argument networks:using graph properties to analyse divisive issues[C]//Proceedings of the 10th international conference on language resources and evaluation. Portoro:LREC, 2016:3899-3906.
[73] LIEBECK M, ESAU K, CONRAD S. What to do with an airport? Mining arguments in the German online participation project tempelhofer feld[C]//Proceedings of the third workshop on argument mining. Berlin:ACL, 2016:144-153.
[74] TEUFEL S, MOENS M. Summarizing scientific articles:Experiments with relevance and rhetorical status[J]. Computational linguistics, 2002, 28(4):409-445.
[75] YEPES A J, MORK J, ARONSON A. Using the argumentative structure of scientific literature to improve information access[C]//Proceedings of the 2013 workshop on biomedical natural language processing. Sofia:ACL, 2013:102-110.
[76] GREEN N. Identifying argumentation schemes in genetics research articles[C]//Proceedings of the 2nd workshop on argumentation mining. Denver:ACL, 2015:12-21.
[77] MAYER T, CABRIO E, VILLATA S. Evidence type classification in Randomized Controlled Trials[C]//Proceedings of the 5th workshop on argument mining. Brussels:ACL, 2018:29-34.