中文    English

Journal of Library and Information Science in Agriculture ›› 2023, Vol. 35 ›› Issue (6): 16-28.doi: 10.13998/j.cnki.issn1002-1248.23-0347

Previous Articles     Next Articles

Research Advances in Argument Mining

LI Jiao1,2, ZHAO Ruixue1,2,4,*, XIAN Guojian1,2,4, HUANG Yongwen1,2, SUN Tan3,4   

  1. 1. Agricultural Information Institute of CAAS, Beijing 100081;
    2. Key Laboratory of Knowledge Mining and Knowledge Services in Agricultural Converging Publishing, National Press and Publication Administration, Beijing 100081;
    3. Chinese Academy of Agricultural Sciences, Beijing 100081;
    4. Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing 100081
  • Received:2023-05-05 Online:2023-06-05 Published:2023-08-02

PDF (PC)

235

Abstract: [Purpose/Significance] Argument mining, a research hotspot in the field of computational linguistics, provides machine processable structured data for computational models of argument. Argument mining tasks are closely related to artificial intelligence (AI) technologies, such as natural language processing and knowledge representation. There are numerous systematic studies in academia and a clear technical realization route has come into being. New research results continue to emerge as a result of rich resources and rapid development and iteration of deep learning, large language models (LLMs), and other technologies. This study, which reviews the research status and progress of argument mining, can serve as a resource for future research and application development. [Method/Process] Through literature review, this paper systematically reviews the relevant research basis (including foundational techniques and semantic representation models), summarizes the related technical system in terms of task framework, influencing factors of technological complexity, and method classification, and then introduces the argument mining practice and application cases for specific fields and research objectives and makes a comparative analysis. Most importantly, the overall development and characteristics of this research field are summarized, with a focus on tracking the progress of multimedia argument mining in the context of the new AI environment. [Results/Conclusions] Relevant research has experienced the development of "machine learning - deep learning" and "text only - multimodal", and the levels of development and application of various fields vary much. Future research may focus on how to achieve multigranularity and multimodal content generalization, as well as how to promote its application and implementation in practice. Possible research directions include: 1) the use of LLMs in argument mining, because they exhibit significant benefits in downstream applications such as natural language processing and multimodal learning, and can also provide certain technical conditions for the generation of argument content; 2) the use of domain knowledge organization systems such as vocabulary, knowledge base and knowledge graph: with these systems, researchers can combine domain-specific argument mining models with rich knowledge structure, to strengthen semantic representation and organization improve the systematization and dig deeper into argument mining model research in the domain; 3) promoting the application research and practice of argument mining in more fields or across disciplines, and improving the retrieval and visualization of argument information, such as combining information retrieval methods with argument mining to build the next generation of argument search engines.

Key words: argument mining, technical system, development path, multimodal

CLC Number: 

  • G254
[1] CABRIO E, VILLATA S.Five years of argument mining: A data-driven analysis[C]// Proceedings of the 27th International Joint Conference on Artificial Intelligence, New York: ACM, 2018: 5427-5433.
[2] HABERNAL I, GUREVYCH I.Argumentation mining in user-generated web discourse[J]. Computational linguistics, 2017, 43(1): 125-179.
[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, New York: ACM, 2007: 225-230.
[4] DOUGLAS W.Argument mining by applying artumentation schemes[J]. Studies in logic, 2011, 4(1): 38-64.
[5] LIU B.Sentiment analysis and subjectivity Bing Liu[M]//Handbook of natural language processing. Chapman and Hall/CRC, 2010: 651-690.
[6] HOGENBOOM A, HOGENBOOM F, KAYMAK U, et al.Mining economic sentiment using argumentation structures[C]//International Conference on Conceptual Modeling, Berlin, Heidelberg: Springer, 2010: 200-209.
[7] RUMSHISKY A, GRONAS M, POTASH P, et al.Combining network and language indicators for tracking conflict intensity[M]// International conference on social informatics. Cham: Springer, 2017: 391-404.
[8] ATHAR A.Sentiment analysis of citations using sentence structure-based features[C]// Proceedings of the ACL 2011 Student Session. New York: ACM, 2011: 81-87.
[9] TOULMIN S E.The uses of argument[M]. Updated ed. Cambridge, UK: Cambridge University Press, 2003.
[10] BUCKINGHAM SHUM S, MOTTA E, DOMINGUE J.ScholOnto: An ontology-based digital library server for research documents and discourse[J]. International journal on digital libraries, 2000, 3(3): 237-248.
[11] VITALI F, PERONI S.The argument model ontology[EB/OL]. [2023-01-11].https://sparontologies.github.io/amo/current/amo.html.
[12] CLARK T, CICCARESE P N, GOBLE C A.Micropublications: A semantic model for claims, evidence, arguments and annotations in biomedical communications[J]. Journal of biomedical semantics, 2014, 5: 28.
[13] CICCARESE P, WU E, WONG G, et al.The SWAN biomedical discourse ontology[J]. Journal of biomedical informatics, 2008, 41(5): 739-751.
[14] 王晓光, 周慧敏, 宋宁远. 科学论文论证本体设计与标注实验[J]. 情报学报, 2020, 39(9): 885-895.
WANG X G, ZHOU H M, SONG N Y.Scientific paper argumentation ontology and annotation experiment[J]. Journal of the China society for scientific and technical information, 2020, 39(9): 885-895.
[15] 曲佳彬, 欧石燕. 语义出版驱动的科学论文论证结构语义建模研究[J]. 现代情报, 2021, 41(12): 48-59.
QU J B, OU S Y.Semantic modeling for scientific paper argumen-tation structure driven by sematic publishing[J]. Journal of modern information, 2021, 41(12): 48-59.
[16] PELDSZUS A, STEDE M.From argument diagrams to argumentation mining in texts[J]. International journal of cognitive informatics and natural intelligence, 2013, 7(1): 1-31.
[17] 石岳峰, 王熠, 张岳. 深度学习在论辩挖掘任务中的应用[J]. 中文信息学报, 2022, 36(7): 1-12, 23.
SHI Y F, WANG Y, ZHANG Y.Deep learning in argument mining: A survey[J]. Journal of Chinese information processing, 2022, 36(7): 1-12, 23.
[18] STAB C, GUREVYCH I.Parsing argumentation structures in persuasive essays[J]. Computational linguistics, 2017, 43(3): 619-659.
[19] LAWRENCE J, REED C.Argument mining: A survey[J]. Computa-tional linguistics, 2020, 45(4): 765-818.
[20] ROCHA G, CARDOSO H L, TEIXEIRA J.ArgMine: A framework for argumentation mining[C]//Computational Processing of the Por-tuguese Language - 12th International Conference, 2016: 13.
[21] LIPPI M, TORRONI P.MARGOT: A web server for argumentation mining[J]. Expert systems with applications, 2016, 65: 292-303.
[22] NICULAE V, PARK J, CARDIE C. Argument mining with struc-tured SVMs and RNNs[J]. arXiv:1704.06869, 2017. https://arxiv.org/abs/1704.06869.
[23] BAR-HAIM R, BHATTACHARYA I, DINUZZO F, et al.Stance classification of context-dependent claims[C]. EACL, 2017: 251-261.
[24] LEVY R, BILU Y, HERSHCOVICH D, et al.Context dependent claim detection[C]. COLING, 2014: 1489-1500.
[25] DUTHIE R, BUDZYNSKA K, REED C.Mining ethos in political debate[C]. COMMA, 2016: 299-310.
[26] STAB C, GUREVYCH I.Identifying argumentative discourse structures in persuasive essays[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2014: 46-56.
[27] 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.
[28] MOCHALES R, MOENS M F.Argumentation mining[J]. Artificial intelligence and law, 2011, 19(1): 1-22.
[29] GOUDAS T, LOUIZOS C, PETASIS G, et al.Argument extraction from news, blogs, and social media[M]//Hellenic conference on artificial intelligence. Cham: Springer, 2014: 287-299.
[30] GROZA A, POPA O M.Mining arguments from cancer documents using natural language processing and ontologies[C]//2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP). Piscataway, New Jersey: IEEE, 2016: 77-84.
[31] WACHSMUTH H, STEIN B, AJJOUR Y."PageRank" for argument relevance[C]//Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017: 1117-1127.
[32] LIPPI M, TORRONI P.Argument mining: A machine learning per-spective[M]//International workshop on theory and applications of formal argumentation. Cham: Springer, 2015: 163-176.
[33] SUHARTONO D, GEMA A P, WINTON S, et al.Argument annotation and analysis using deep learning with attention mechanism in Bahasa Indonesia[J]. Journal of big data, 2020, 7(1): 1-18.
[34] COCARASCU O, TONI F.Identifying attack and support argumenta-tive relations using deep learning[C]//Proceedings of the 2017 Con-ference on Empirical Methods in Natural Language Processing. Stroudsburg, PA, USA: Association for Computational Linguistics, 2017: 1374-1379.
[35] COCARASCU O, TONI F.Combining deep learning and argumentative reasoning for the analysis of social media textual content using small data sets[J]. Computational linguistics, 2018, 44(4): 833-858.
[36] NICULAE V, PARK J, CARDIE C.Argument mining with structured SVMs and RNNs[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2017: 985-995.
[37] 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. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018: 1-10.
[38] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, New Jersey: IEEE, 2016: 770-778.
[39] DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv:1810.04805, 2018. https://arxiv.org/abs/1810.04805.
[40] ZHANG G C, NULTY P, LILLIS D. Enhancing legal argument mining with domain pre-training and neural networks[J]. arXiv:2202.13457, 2022. https://arxiv.org/abs/2202.13457.
[41] SRIVASTAVA P, BHATNAGAR P, GOEL A.Argument Mining using BERT and Self-Attention based Embeddings[C]//2022 4th International Conference on Advances in Computing, Communication Control and Networking(ICAC3N). Piscataway, New Jersey: IEEE, 2023: 1536-1540.
[42] WILLIAM H, SUGANDA G A.CNN-BERT for measuring agreement between argument in online discussion[J]. International journal of web information systems, 2022, 18(5/6): 356-368.
[43] 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. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019: 567-578.
[44] TERUEL M, CARDELLINO C, CARDELLINO F, et al.Increasing argument annotation reproducibility by using inter-annotator agreement to improve guidelines[C]. LREC, 2018: 4061-4064.
[45] GRABMAIR M, ASHLEY K D, CHEN R, et al.Introducing LUIMA: An experiment in legal conceptual retrieval of vaccine injury decisions using a UIMA type system and tools[C]//Proceedings of the 15th International Conference on Artificial Intelligence and Law. New York: ACM, 2015: 69-78.
[46] FREEMAN J B.A theory of argument structure[M]. Berlin; New York: Foris Publications, 1991.
[47] WALTON D.Argumentation theory: A very short introduction[M]// Argumentation in artificial intelligence. Boston, MA: Springer US, 2009: 1-22.
[48] IBEKE E, LIN C H, WYNER A, et al.Extracting and understanding contrastive opinion through topic relevant sentences[C]. IJCNLP, 2017: 395-400.
[49] DUSMANU M, CABRIO E, VILLATA S.Argument mining on twitter: Arguments, facts and sources[C]. EMNLP, 2017: 2317-2322.
[50] LAI M, CIGNARELLA A T, HERNáNDEZ FARíAS D I, et al. Multilingual stance detection in social media political debates[J]. Computer speech & language, 2020, 63: 101075.
[51] FABBRI A R, RAHMAN F, RIZVI I, et al. ConvoSumm: Conversation summarization benchmark and improved abstractive summarization with argument mining[J]. arXiv:2106.00829, 2021. https://arxiv.org/abs/2106.00829.
[52] LIPPI M, TORRONI P.Argument mining from speech: Detecting claims in political debates[C]. Proceedings of the AAAI conference on artificial intelligence, 2016, 30(1): 2979-2985.
[53] NADERI N, HIRST G.Argumentation mining in parliamentary dis-course[M]//Principles and practice of multi-agent systems. Cham: Springer International Publishing, 2016: 16-25.
[54] MENINI S, CABRIO E, TONELLI S, et al.Never retreat, never retract: Argumentation analysis for political speeches[C]. Proceed-ings of the AAAI conference on artificial intelligence, 2018, 32(1): 4889-4896.
[55] HUA X Y, WANG L. Understanding and detecting supporting argu-ments of diverse types[EB/OL]. arXiv:1705.00045, 2017. https://arxiv.org/abs/1705.00045.
[56] STAB C, MILLER T, GUREVYCH I. Cross-topic argument mining from heterogeneous sources using attention-based neural networks[J]. arXiv:1802.05758, 2018. https://arxiv.org/abs/1802.05758.
[57] BINDER A, VERMA B, HENNIG L. Full-text argumentation min-ing on scientific publications[J]. arXiv:2210.13084, 2022. https://arxiv.org/abs/2210.13084.
[58] TEUFEL S.Argumentative zoning: Information extraction from sci-entific text[D]. Scotland: University of Edinburgh, 1999. Argumentative zoning: Information extraction from sci-entific text[D]. Scotland: University of Edinburgh, 1999. http://hdl.handle.net/1842/11456.
[59] TEUFEL S, SIDDHARTHAN A, BATCHELOR C R.Towards domain-independent argumentative zoning: Evidence from chemistry and computational linguistics[C]. EMNLP, 2009: 1493-1502.
[60] TEUFEL S.The structure of scientific articles - Applications to citation indexing and summarization[J]. Studies in computational linguistics, 2010, 38(2): 443-445.
[61] GREEN N L.Towards mining scientific discourse using argumentation schemes[J]. Argument & computation, 2018, 9(2): 121-135.
[62] LAUSCHER A, GLAVA? G, PONZETTO S P. An argument-anno-tated corpus of scientific publications[C]//Proceedings of the 5th Workshop on Argument Mining. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018: 40-46.
[63] FISAS B, RONZANO F, SAGGION H.A multi-layered annotated corpus of scientific papers[C]//Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), 2016:3081-3088.
[64] WANG X G, SONG N Y, ZHOU H M, et al.The representation of argumentation in scientific papers: A comparative analysis of two research areas[J]. Journal of the association for information science and technology, 2022, 73(6): 863-878.
[65] LAUSCHER A, GLAVA? G, ECKERT K. ArguminSci: A tool for analyzing argumentation and rhetorical aspects in scientific writing[C]//Proceedings of the 5th Workshop on Argument Mining. Stroudsburg, PA, USA: Association for Computational Linguistics, 2018: 22-28.
[66] 牛丽慧, 欧石燕. 基于论证结构的科学论文内容呈现模式研究[J]. 情报理论与实践, 2022, 45(10): 155-163.
NIU L H, OU S Y.Research on the content presentation mode of sci-entific articles based on argument structure[J]. Information studies: Theory & application, 2022, 45(10): 155-163.
[67] LIPPI M, TORRONI P.Argumentation mining: State of the art and emerging trends[J]. ACM transactions on Internet technology, 16(2): 1-25.
[68] MESTRE R, MILICIN R, MIDDLETON S, et al.M-arg: Multimodal argument mining dataset for political debates with audio and tran-scripts[C]//Proceedings of the 8th Workshop on Argument Mining. Stroudsburg, PA, USA: Association for Computational Linguistics, 2021: 78-88.
[69] MANCINI E, RUGGERI F, ANDREA G, et al.Multimodal argu-ment mining: A case study in political debates[C]//Proceedings of the 9th Workshop on Argument Mining, Co-Located with the 29th International Conference on Computational Linguistic, 2022: 158-170.
[70] OpenAI. GPT-4 technical report[J]. arXiv preprint arXiv:2303. 08774, 2023.
[71] POJONI M L, DUMANI L, SCHENKEL R.Argument-mining from podcasts using ChatGPT[C]//ICCBR TMG'23: Workshop on Text Mining and Generation at ICCBR2023, 2023.
[1] XIAO Man, WANG Xuan, WANG Fang, HUANG Jinxia. Framework and Development Path of Open Science Capability [J]. Journal of Library and Information Science in Agriculture, 2022, 34(9): 15-28.
[2] TAN Yawen. An Analysis on the Gamification Development Path of Library Open Data [J]. Journal of Library and Information Science in Agriculture, 2022, 34(6): 83-92.
[3] WANG Yi. Theoretical Mode and Implementation Path of Cultural and Creative Product Development in Public Libraries [J]. Journal of Library and Information Science in Agriculture, 2020, 32(11): 5-14.
[4] YU Le. Research on the Application of "LibQUAL+TM" in Digital Library's Information Service Quality Evaluation [J]. , 2018, 30(2): 176-179.
[5] GAO Peifeng. Research on the Service Mode Innovation of Library Reading Promotion from the Perspective of Reading Cycle Theory [J]. , 2017, 29(8): 197-200.
[6] ZHAO Qing-ling, CHENG Yu-qin, ZHAO Yuan, CHEN Ji-xiang. Study on the Technical System and Application of Domestic Knowledge Collaboration Based on CNKI [J]. , 2015, 27(10): 10-14.
[7] ZHANG Yun. Make a thorough inquiry on way of Librarians’ Training and Development——In the construction of University Digital Library as an example [J]. , 2014, 26(4): 197-200.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Editorial Board of Journal of Library and Information Science in Agriculture
Tel：(010)82109667　Fax：(010)82109667　E-mail：xuekan＠caas.cn
Powered by Beijing Magtech Co., Ltd.
知识共享许可协议