DEBISS-Arg: An In Depth Data Annotation Protocol and Corpus for Argument Mining in Semi Structured Debates
Abstract
Argumentation plays a crucial role across various domains of human activity. However, its diverse applications present challenges in knowledge representation, leading to the development of numerous data models without a universally accepted standard. This lack of standardization complicates the creation of data and representation frameworks for argument annotation, which are essential for building high-quality datasets. This research addresses these limitations by proposing a comprehensive data annotation protocol specifically designed for argument mining in semi-structured debates. The protocol is applied to the newly introduced DEBISS-Arg corpus, which includes multiple annotation labels covering a range of argument mining tasks in Brazilian Portuguese.References
Abkenar, M. Y., Stede, M., and Oepen, S. (2021). Neural argumentation mining on essays and microtexts with contextualized word embeddings (short paper). In Swiss Text Analytics Conference.
Accuosto, P., Neves, M. L., and Saggion, H. (2021). Argumentation mining in scientific literature: From computational linguistics to biomedicine. In BIR@ECIR.
Aharoni, E., Polnarov, A., Lavee, T., Hershcovich, D., Levy, R., Rinott, R., Gutfreund, D., and Slonim, N. (2014). A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics. In Green, N., Ashley, K., Litman, D., Reed, C., and Walker, V., editors, Proceedings of the First Workshop on Argumentation Mining, pages 64–68, Baltimore, Maryland. Association for Computational Linguistics.
Al Khatib, K., Ghosal, T., Hou, Y., de Waard, A., and Freitag, D. (2021). Argument mining for scholarly document processing: Taking stock and looking ahead. In Beltagy, I., Cohan, A., Feigenblat, G., Freitag, D., Ghosal, T., Hall, K., Herrmannova, D., Knoth, P., Lo, K., Mayr, P., Patton, R. M., Shmueli-Scheuer, M., de Waard, A., Wang, K., and Wang, L. L., editors, Proceedings of the Second Workshop on Scholarly Document Processing, pages 56–65, Online. Association for Computational Linguistics.
Bar-Haim, R., Ein-Dor, L., Orbach, M., Venezian, E., and Slonim, N. (2021). Advances in debating technologies: Building AI that can debate humans. In Chiang, D. and Zhang, M., editors, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts, pages 1–5, Online. Association for Computational Linguistics.
Bentahar, J., Moulin, B., and Bélanger, M. (2010). A taxonomy of argumentation models used for knowledge representation. Artificial Intelligence Review, 33(3):211–259.
Bhatti, M. M. A., Ahmad, A. S., and Park, J. (2021). Argument mining on Twitter: A case study on the planned parenthood debate. In Al-Khatib, K., Hou, Y., and Stede, M., editors, Proceedings of the 8th Workshop on Argument Mining, pages 1–11, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Binder, A., Verma, B., and Hennig, L. (2022). Full-text argumentation mining on scientific publications.
Boltužić, F. and Šnajder, J. (2016). Fill the gap! analyzing implicit premises between claims from online debates. In Reed, C., editor, Proceedings of the Third Workshop on Argument Mining (ArgMining2016), pages 124–133, Berlin, Germany. Association for Computational Linguistics.
Budzynska, K. and Reed, C. (2011). Speech acts of argumentation: inference anchors and peripheral cues in dialogue. In Proceedings of the 10th AAAI Conference on Computational Models of Natural Argument, AAAIWS’11-10, page 3–10. AAAI Press.
Cabrio, E. and Villata, S. (2018). Five years of argument mining: a data-driven analysis. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pages 5427–5433. International Joint Conferences on Artificial Intelligence Organization.
Chakrabarty, T., Hidey, C., Muresan, S., McKeown, K., and Hwang, A. (2019). AMPERSAND: Argument mining for PERSuAsive oNline discussions. In Inui, K., Jiang, J., Ng, V., and Wan, X., editors, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2933–2943, Hong Kong, China. Association for Computational Linguistics.
Chakrabarty, T., Hidey, C., Muresan, S., Mckeown, K., and Hwang, A. (2020). Ampersand: Argument mining for persuasive online discussions.
Daxenberger, J., Eger, S., Habernal, I., Stab, C., and Gurevych, I. (2017). What is the essence of a claim? cross-domain claim identification. In Palmer, M., Hwa, R., and Riedel, S., editors, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2055–2066, Copenhagen, Denmark. Association for Computational Linguistics.
Duthie, R., Budzynska, K., and Reed, C. (2016). Mining Ethos in Political Debate, volume 287 of Frontiers in Artificial Intelligence and Applications, pages 299–310. IOS Press, Netherlands. This research was supported in part by EPSRC in the UK under grant EP/M506497/1 and in part by the Polish National Science Centre under grant 2015/18/M/HS1/00620.
Feger, M. and Dietze, S. (2024). Taco – twitter arguments from conversations.
Fergadis, A., Pappas, D., Karamolegkou, A., and Papageorgiou, H. (2021). Argumentation mining in scientific literature for sustainable development. In Al-Khatib, K., Hou, Y., and Stede, M., editors, Proceedings of the 8th Workshop on Argument Mining, pages 100–111, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Gao, Y. (2024). Mining Arguments in Scientific Documents. Doctoral thesis, ETH Zurich, Zurich.
Guo, K., Li, Y., Li, Y., and Chu, S. (2024). Understanding efl students’ chatbot-assisted argumentative writing: An activity theory perspective. 29(1):1–20. Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Habernal, I. and Gurevych, I. (2015). Exploiting debate portals for semi-supervised argumentation mining in user-generated web discourse. In Màrquez, L., Callison-Burch, C., and Su, J., editors, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2127–2137, Lisbon, Portugal. Association for Computational Linguistics.
Habernal, I. and Gurevych, I. (2016). Which argument is more convincing? analyzing and predicting convincingness of web arguments using bidirectional LSTM. In Erk, K. and Smith, N. A., editors, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1589–1599, Berlin, Germany. Association for Computational Linguistics.
Habernal, I. and Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics, 43(1):125–179.
Haddadan, S., Cabrio, E., and Villata, S. (2019). Yes, we can! mining arguments in 50 years of US presidential campaign debates. In Korhonen, A., Traum, D., and Màrquez, L., editors, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4684–4690, Florence, Italy. Association for Computational Linguistics.
Hautli-Janisz, A., Kikteva, Z., Siskou, W., Gorska, K., Becker, R., and Reed, C. (2022). Qt30: A corpus of argument and conflict in broadcast debate. In Proceedings of the 13th Language Resources and Evaluation Conference, pages 3291–3300. European Language Resources Association (ELRA). © European Language Resources Association (ELRA).
Kotelnikov, E., Loukachevitch, N., Nikishina, I., and Panchenko, A. (2022). Ruarg-2022: Argument mining evaluation. In Computational Linguistics and Intellectual Technologies. RSUH.
Lavee, T., Orbach, M., Kotlerman, L., Kantor, Y., Gretz, S., Dankin, L., Jacovi, M., Bilu, Y., Aharonov, R., and Slonim, N. (2019). Towards effective rebuttal: Listening comprehension using corpus-wide claim mining. In Stein, B. and Wachsmuth, H., editors, Proceedings of the 6th Workshop on Argument Mining, pages 58–66, Florence, Italy. Association for Computational Linguistics.
Lawrence, J. and Reed, C. (2020). Argument Mining: A Survey. Computational Linguistics, 45(4):765–818.
Lima, P. L. and Campelo, C. E. (2024). Disfluency detection and removal in speech transcriptions via large language models. In Anais do XV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, pages 227–235, Porto Alegre, RS, Brasil. SBC.
Lippi, M. and Torroni, P. (2016). Argument mining from speech: Detecting claims in political debates. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1).
Mancini, E., Ruggeri, F., Galassi, A., and Torroni, P. (2022). Multimodal argument mining: A case study in political debates. In Lapesa, G., Schneider, J., Jo, Y., and Saha, S., editors, Proceedings of the 9th Workshop on Argument Mining, pages 158–170, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Mestre, R., Milicin, R., Middleton, S. E., Ryan, M., Zhu, J., and Norman, T. J. (2021). M-arg: Multimodal argument mining dataset for political debates with audio and transcripts. In Al-Khatib, K., Hou, Y., and Stede, M., editors, Proceedings of the 8th Workshop on Argument Mining, pages 78–88, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Michael, Fromm, Max, Berrendorf, Evgeniy, Faerman, and Thomas, Seidl (2020). Argument mining driven analysis of peer-reviews dataset.
Mirkin, S., Jacovi, M., Lavee, T., Kuo, H.-K., Thomas, S., Sager, L., Kotlerman, L., Venezian, E., and Slonim, N. (2018). A recorded debating dataset. In Calzolari, N., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Hasida, K., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S., and Tokunaga, T., editors, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
Pojoni, M.-L., Dumani, L., and Schenkel, R. (2023). Argument-mining from podcasts using chatgpt. In ICCBR Workshops, pages 129–144.
Reed, C. and Norman, T., editors (2003). Argumentation Machines: New Frontiers in Argument and Computation. Argumentation Library. Kluwer Academic Publishers, Netherlands.
Sazid, M. T. and Mercer, R. E. (2022). A unified representation and a decoupled deep learning architecture for argumentation mining of students’ persuasive essays. In Lapesa, G., Schneider, J., Jo, Y., and Saha, S., editors, Proceedings of the 9th Workshop on Argument Mining, pages 74–83, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Sousa, J. P., Nascimento, R., Araujo, R., and Coelho, O. (2021). Não se perca no debate! mineração de argumentação em redes sociais. In Anais do X Brazilian Workshop on Social Network Analysis and Mining, pages 139–150, Porto Alegre, RS, Brasil. SBC.
Souza, K., Pereira, D., and Claúdio, C. (2025). Debiss: a corpus of individual, semistructured and spoken debates. In Anais do XVI Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. SBC.
Stylianou, N. and Vlahavas, I. (2021). Transformed: End-to-nd transformers for evidencebased medicine and argument mining in medical literature. Journal of Biomedical Informatics, 117:103767.
van Eemeren, F. H., Garssen, B., Krabbe, E. C. W., Snoeck Henkemans, A. F., Verheij, B., and Wagemans, J. H. M. (2014). Handbook of argumentation theory.
Visser, J., Lawrence, J., Wagemans, J., and Reed, C. (2019). An annotated corpus of argument schemes in us election debates. In Proceedings of the 9th Conference of the International Society for the Study of Argumentation (ISSA), 3-6 July 2018, pages 1101–1111.
Walton, D., Reed, C., and Macagno, F. (2008). Argumentation Schemes. Cambridge University Press.
Wambsganss, T., Niklaus, C., Cetto, M., Söllner, M., Handschuh, S., and Leimeister, J. M. (2020a). Al: An adaptive learning support system for argumentation skills. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20, page 1–14, New York, NY, USA. Association for Computing Machinery.
Wambsganss, T., Niklaus, C., Söllner, M., Handschuh, S., and Leimeister, J. M. (2020b). A corpus for argumentative writing support in german.
Wang, S., Zhang, Y., and Du, J. (2024). Utilizing llms to evaluate the argument quality of triples in semmeddb for enhanced understanding of disease mechanisms. medRxiv.
Westermann, H., Savelka, J., Walker, V. R., Ashley, K. D., and Benyekhlef, K. (2022). Toward an intelligent tutoring system for argument mining in legal texts.
Zhang, G., Nulty, P., and Lillis, D. (2022). Enhancing legal argument mining with domain pre-training and neural networks.
Zhang, G., Nulty, P., and Lillis, D. (2023). Argument mining with graph representation learning. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, ICAIL ’23, page 371–380, New York, NY, USA. Association for Computing Machinery.
Accuosto, P., Neves, M. L., and Saggion, H. (2021). Argumentation mining in scientific literature: From computational linguistics to biomedicine. In BIR@ECIR.
Aharoni, E., Polnarov, A., Lavee, T., Hershcovich, D., Levy, R., Rinott, R., Gutfreund, D., and Slonim, N. (2014). A benchmark dataset for automatic detection of claims and evidence in the context of controversial topics. In Green, N., Ashley, K., Litman, D., Reed, C., and Walker, V., editors, Proceedings of the First Workshop on Argumentation Mining, pages 64–68, Baltimore, Maryland. Association for Computational Linguistics.
Al Khatib, K., Ghosal, T., Hou, Y., de Waard, A., and Freitag, D. (2021). Argument mining for scholarly document processing: Taking stock and looking ahead. In Beltagy, I., Cohan, A., Feigenblat, G., Freitag, D., Ghosal, T., Hall, K., Herrmannova, D., Knoth, P., Lo, K., Mayr, P., Patton, R. M., Shmueli-Scheuer, M., de Waard, A., Wang, K., and Wang, L. L., editors, Proceedings of the Second Workshop on Scholarly Document Processing, pages 56–65, Online. Association for Computational Linguistics.
Bar-Haim, R., Ein-Dor, L., Orbach, M., Venezian, E., and Slonim, N. (2021). Advances in debating technologies: Building AI that can debate humans. In Chiang, D. and Zhang, M., editors, Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Tutorial Abstracts, pages 1–5, Online. Association for Computational Linguistics.
Bentahar, J., Moulin, B., and Bélanger, M. (2010). A taxonomy of argumentation models used for knowledge representation. Artificial Intelligence Review, 33(3):211–259.
Bhatti, M. M. A., Ahmad, A. S., and Park, J. (2021). Argument mining on Twitter: A case study on the planned parenthood debate. In Al-Khatib, K., Hou, Y., and Stede, M., editors, Proceedings of the 8th Workshop on Argument Mining, pages 1–11, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Binder, A., Verma, B., and Hennig, L. (2022). Full-text argumentation mining on scientific publications.
Boltužić, F. and Šnajder, J. (2016). Fill the gap! analyzing implicit premises between claims from online debates. In Reed, C., editor, Proceedings of the Third Workshop on Argument Mining (ArgMining2016), pages 124–133, Berlin, Germany. Association for Computational Linguistics.
Budzynska, K. and Reed, C. (2011). Speech acts of argumentation: inference anchors and peripheral cues in dialogue. In Proceedings of the 10th AAAI Conference on Computational Models of Natural Argument, AAAIWS’11-10, page 3–10. AAAI Press.
Cabrio, E. and Villata, S. (2018). Five years of argument mining: a data-driven analysis. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18, pages 5427–5433. International Joint Conferences on Artificial Intelligence Organization.
Chakrabarty, T., Hidey, C., Muresan, S., McKeown, K., and Hwang, A. (2019). AMPERSAND: Argument mining for PERSuAsive oNline discussions. In Inui, K., Jiang, J., Ng, V., and Wan, X., editors, Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2933–2943, Hong Kong, China. Association for Computational Linguistics.
Chakrabarty, T., Hidey, C., Muresan, S., Mckeown, K., and Hwang, A. (2020). Ampersand: Argument mining for persuasive online discussions.
Daxenberger, J., Eger, S., Habernal, I., Stab, C., and Gurevych, I. (2017). What is the essence of a claim? cross-domain claim identification. In Palmer, M., Hwa, R., and Riedel, S., editors, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2055–2066, Copenhagen, Denmark. Association for Computational Linguistics.
Duthie, R., Budzynska, K., and Reed, C. (2016). Mining Ethos in Political Debate, volume 287 of Frontiers in Artificial Intelligence and Applications, pages 299–310. IOS Press, Netherlands. This research was supported in part by EPSRC in the UK under grant EP/M506497/1 and in part by the Polish National Science Centre under grant 2015/18/M/HS1/00620.
Feger, M. and Dietze, S. (2024). Taco – twitter arguments from conversations.
Fergadis, A., Pappas, D., Karamolegkou, A., and Papageorgiou, H. (2021). Argumentation mining in scientific literature for sustainable development. In Al-Khatib, K., Hou, Y., and Stede, M., editors, Proceedings of the 8th Workshop on Argument Mining, pages 100–111, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Gao, Y. (2024). Mining Arguments in Scientific Documents. Doctoral thesis, ETH Zurich, Zurich.
Guo, K., Li, Y., Li, Y., and Chu, S. (2024). Understanding efl students’ chatbot-assisted argumentative writing: An activity theory perspective. 29(1):1–20. Publisher Copyright: © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
Habernal, I. and Gurevych, I. (2015). Exploiting debate portals for semi-supervised argumentation mining in user-generated web discourse. In Màrquez, L., Callison-Burch, C., and Su, J., editors, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2127–2137, Lisbon, Portugal. Association for Computational Linguistics.
Habernal, I. and Gurevych, I. (2016). Which argument is more convincing? analyzing and predicting convincingness of web arguments using bidirectional LSTM. In Erk, K. and Smith, N. A., editors, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1589–1599, Berlin, Germany. Association for Computational Linguistics.
Habernal, I. and Gurevych, I. (2017). Argumentation Mining in User-Generated Web Discourse. Computational Linguistics, 43(1):125–179.
Haddadan, S., Cabrio, E., and Villata, S. (2019). Yes, we can! mining arguments in 50 years of US presidential campaign debates. In Korhonen, A., Traum, D., and Màrquez, L., editors, Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4684–4690, Florence, Italy. Association for Computational Linguistics.
Hautli-Janisz, A., Kikteva, Z., Siskou, W., Gorska, K., Becker, R., and Reed, C. (2022). Qt30: A corpus of argument and conflict in broadcast debate. In Proceedings of the 13th Language Resources and Evaluation Conference, pages 3291–3300. European Language Resources Association (ELRA). © European Language Resources Association (ELRA).
Kotelnikov, E., Loukachevitch, N., Nikishina, I., and Panchenko, A. (2022). Ruarg-2022: Argument mining evaluation. In Computational Linguistics and Intellectual Technologies. RSUH.
Lavee, T., Orbach, M., Kotlerman, L., Kantor, Y., Gretz, S., Dankin, L., Jacovi, M., Bilu, Y., Aharonov, R., and Slonim, N. (2019). Towards effective rebuttal: Listening comprehension using corpus-wide claim mining. In Stein, B. and Wachsmuth, H., editors, Proceedings of the 6th Workshop on Argument Mining, pages 58–66, Florence, Italy. Association for Computational Linguistics.
Lawrence, J. and Reed, C. (2020). Argument Mining: A Survey. Computational Linguistics, 45(4):765–818.
Lima, P. L. and Campelo, C. E. (2024). Disfluency detection and removal in speech transcriptions via large language models. In Anais do XV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana, pages 227–235, Porto Alegre, RS, Brasil. SBC.
Lippi, M. and Torroni, P. (2016). Argument mining from speech: Detecting claims in political debates. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1).
Mancini, E., Ruggeri, F., Galassi, A., and Torroni, P. (2022). Multimodal argument mining: A case study in political debates. In Lapesa, G., Schneider, J., Jo, Y., and Saha, S., editors, Proceedings of the 9th Workshop on Argument Mining, pages 158–170, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Mestre, R., Milicin, R., Middleton, S. E., Ryan, M., Zhu, J., and Norman, T. J. (2021). M-arg: Multimodal argument mining dataset for political debates with audio and transcripts. In Al-Khatib, K., Hou, Y., and Stede, M., editors, Proceedings of the 8th Workshop on Argument Mining, pages 78–88, Punta Cana, Dominican Republic. Association for Computational Linguistics.
Michael, Fromm, Max, Berrendorf, Evgeniy, Faerman, and Thomas, Seidl (2020). Argument mining driven analysis of peer-reviews dataset.
Mirkin, S., Jacovi, M., Lavee, T., Kuo, H.-K., Thomas, S., Sager, L., Kotlerman, L., Venezian, E., and Slonim, N. (2018). A recorded debating dataset. In Calzolari, N., Choukri, K., Cieri, C., Declerck, T., Goggi, S., Hasida, K., Isahara, H., Maegaard, B., Mariani, J., Mazo, H., Moreno, A., Odijk, J., Piperidis, S., and Tokunaga, T., editors, Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).
Pojoni, M.-L., Dumani, L., and Schenkel, R. (2023). Argument-mining from podcasts using chatgpt. In ICCBR Workshops, pages 129–144.
Reed, C. and Norman, T., editors (2003). Argumentation Machines: New Frontiers in Argument and Computation. Argumentation Library. Kluwer Academic Publishers, Netherlands.
Sazid, M. T. and Mercer, R. E. (2022). A unified representation and a decoupled deep learning architecture for argumentation mining of students’ persuasive essays. In Lapesa, G., Schneider, J., Jo, Y., and Saha, S., editors, Proceedings of the 9th Workshop on Argument Mining, pages 74–83, Online and in Gyeongju, Republic of Korea. International Conference on Computational Linguistics.
Sousa, J. P., Nascimento, R., Araujo, R., and Coelho, O. (2021). Não se perca no debate! mineração de argumentação em redes sociais. In Anais do X Brazilian Workshop on Social Network Analysis and Mining, pages 139–150, Porto Alegre, RS, Brasil. SBC.
Souza, K., Pereira, D., and Claúdio, C. (2025). Debiss: a corpus of individual, semistructured and spoken debates. In Anais do XVI Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana. SBC.
Stylianou, N. and Vlahavas, I. (2021). Transformed: End-to-nd transformers for evidencebased medicine and argument mining in medical literature. Journal of Biomedical Informatics, 117:103767.
van Eemeren, F. H., Garssen, B., Krabbe, E. C. W., Snoeck Henkemans, A. F., Verheij, B., and Wagemans, J. H. M. (2014). Handbook of argumentation theory.
Visser, J., Lawrence, J., Wagemans, J., and Reed, C. (2019). An annotated corpus of argument schemes in us election debates. In Proceedings of the 9th Conference of the International Society for the Study of Argumentation (ISSA), 3-6 July 2018, pages 1101–1111.
Walton, D., Reed, C., and Macagno, F. (2008). Argumentation Schemes. Cambridge University Press.
Wambsganss, T., Niklaus, C., Cetto, M., Söllner, M., Handschuh, S., and Leimeister, J. M. (2020a). Al: An adaptive learning support system for argumentation skills. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, CHI ’20, page 1–14, New York, NY, USA. Association for Computing Machinery.
Wambsganss, T., Niklaus, C., Söllner, M., Handschuh, S., and Leimeister, J. M. (2020b). A corpus for argumentative writing support in german.
Wang, S., Zhang, Y., and Du, J. (2024). Utilizing llms to evaluate the argument quality of triples in semmeddb for enhanced understanding of disease mechanisms. medRxiv.
Westermann, H., Savelka, J., Walker, V. R., Ashley, K. D., and Benyekhlef, K. (2022). Toward an intelligent tutoring system for argument mining in legal texts.
Zhang, G., Nulty, P., and Lillis, D. (2022). Enhancing legal argument mining with domain pre-training and neural networks.
Zhang, G., Nulty, P., and Lillis, D. (2023). Argument mining with graph representation learning. In Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law, ICAIL ’23, page 371–380, New York, NY, USA. Association for Computing Machinery.
Published
2025-09-29
How to Cite
PEREIRA, David Eduardo; SIMÃO, Daniela Thuaslar; CAMPELO, Claudio E. C..
DEBISS-Arg: An In Depth Data Annotation Protocol and Corpus for Argument Mining in Semi Structured Debates. In: BRAZILIAN SYMPOSIUM IN INFORMATION AND HUMAN LANGUAGE TECHNOLOGY (STIL), 16. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 334-348.
DOI: https://doi.org/10.5753/stil.2025.37836.
