{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,4]],"date-time":"2026-02-04T02:38:28Z","timestamp":1770172708012,"version":"3.49.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T00:00:00Z","timestamp":1587513600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2020,8,31]]},"abstract":"<jats:p>The argumentation in academic writings is necessary to clearly communicate the ideas of the students. The relations between argumentative components are an essential part since this shows the contrast or support of the presented ideas. In this paper, we present two approaches to relation identification between pairs of components. In the first, we detect initially which components are related, to later classify them in support or attack relation. In the second approach, we identify directly which components have a support relation. For these approaches, we employed machine learning techniques with representations of several lexical, syntactic, semantic, structural and indicator features. Experiments in argumentative sections of academic theses showed that the models achieve encouraging results solving the task, and revealing the argumentative structures prevailing in student writings.<\/jats:p>","DOI":"10.3233\/jifs-179874","type":"journal-article","created":{"date-parts":[[2020,4,24]],"date-time":"2020-04-24T11:37:37Z","timestamp":1587728257000},"page":"2081-2091","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Argumentative relation identification in academic texts"],"prefix":"10.1177","volume":"39","author":[{"given":"Jes\u00fas Miguel","family":"Garc\u00eda-Gorrostieta","sequence":"first","affiliation":[{"name":"Computational Sciences Department, Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica, Tonantzintla, Puebla, M\u00e9xico"}]},{"given":"Aurelio","family":"L\u00f3pez-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Computational Sciences Department, Instituto Nacional de Astrof\u00edsica, \u00d3ptica y Electr\u00f3nica, Tonantzintla, Puebla, M\u00e9xico"}]},{"given":"Samuel","family":"Gonz\u00e1lez-L\u00f3pez","sequence":"additional","affiliation":[{"name":"Department of Information Technologies and Communication, Technological University of Nogales, Sonora, M\u00e9xico"}]}],"member":"179","published-online":{"date-parts":[[2020,4,22]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Al-RfouR. PerozziB. and SkienaS. Polyglot: Distributed word representations for multilingual nlp. In Procs of the 17th Conf on Computational Natural Language Learning pp. 183\u2013192 Sofia Bulgaria August 2013. ACL."},{"key":"e_1_3_2_3_2","unstructured":"CabrioE. and VillataS. Combining textual entailment and argumentation theory for supporting online debates interactions. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2 ACL \u201912 pp. 208\u2013212. Association for Computational Linguistics 2012."},{"key":"e_1_3_2_4_2","unstructured":"CapaldiN. C\u00f3mo ganar una discusi\u00f3n. Gedisa 1990."},{"key":"e_1_3_2_5_2","doi-asserted-by":"crossref","unstructured":"CarstensL. and ToniF. Towards relation based argumentation mining. In Procs of the 2nd Workshop on Argumentation Mining pp. 29\u201334. ACL June 2015.","DOI":"10.3115\/v1\/W15-0504"},{"key":"e_1_3_2_6_2","unstructured":"Chris ReedG.R. PalauRaquel Mochales and MoensM.-F. Language resources for studying argument. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC\u201908) pp. 2613\u20132618 Marrakech Morocco may 2008. European Language Resources Association (ELRA)."},{"key":"e_1_3_2_7_2","doi-asserted-by":"crossref","unstructured":"CocarascuO. and ToniF. Identifying attack and support argumentative relations using deep learning. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017) pp. 1374\u20131379. Association for Computational Linguistics.","DOI":"10.18653\/v1\/D17-1144"},{"key":"e_1_3_2_8_2","doi-asserted-by":"crossref","unstructured":"FreemanJ.B. Argument Structure: Representation and Theory. Springer 2011.","DOI":"10.1007\/978-94-007-0357-5"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.3233\/JIFS-169488"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/cae.21996"},{"key":"e_1_3_2_11_2","unstructured":"Gonz\u00e1lez-L\u00f3pezS. and L\u00f3pez-L\u00f3pezA. Colecci\u00f3n de tesis y propuesta de investigaci\u00f3n en tics: un recurso para su an\u00e1lisis y estudio. In XIII Congreso Nacional de Investigaci\u00f3n Educativa (2015) pp. 1\u201315."},{"key":"e_1_3_2_12_2","doi-asserted-by":"crossref","unstructured":"GreenN. Identifying argumentation schemes in genetics research articles. In Proceedings of the 2nd Workshop on Argumentation Mining pp. 12\u201321. Association for Computational Linguistics June 2015.","DOI":"10.3115\/v1\/W15-0502"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1162\/COLI_a_00276"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.2307\/2529310"},{"key":"e_1_3_2_15_2","doi-asserted-by":"crossref","unstructured":"LawrenceJ. and ReedC. Combining argument mining techniques. In Proceedings of the 2nd Workshop on Argumentation Mining pp. 127\u2013136 Denver CO June 2015. Association for Computational Linguistics.","DOI":"10.3115\/v1\/W15-0516"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1145\/2850417"},{"key":"e_1_3_2_17_2","unstructured":"L\u00f3pez FerreroC. and Garc\u00eda NegroniM. La argumentaci\u00f3n en los g\u00e9neros acad\u00e9micos. In Actas del Congreso Internacional La Argumentaci\u00f3n (2003) pp. 1121\u20131129. Universidad de Buenos Aires Buenos Aires."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","unstructured":"MeniniS. CabrioE. TonelliS. and VillataS. Never retreat never retract: Argumentation analysis for political speeches. In Thirty-Second AAAI Conference on Artificial Intelligence (2018) pp. 4889\u20134896. Association for the Advancement of Artificial Intelligence Press.","DOI":"10.1609\/aaai.v32i1.11920"},{"key":"e_1_3_2_19_2","unstructured":"MikolovT. ChenK. CorradoG.S. and DeanJ. Efficient estimation of word representations in vector space CoRR abs\/1301.3781 2013."},{"key":"e_1_3_2_20_2","unstructured":"MochalesR. and MoensM.-F. Study on the structure of argumentation in case law. In Procs of the 2008 Conference on Legal Knowledge and Information Systems (2008) pp. 11\u201320. IOS Press."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10506-010-9104-x"},{"key":"e_1_3_2_22_2","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa F.","year":"2011","unstructured":"PedregosaF., VaroquauxG., GramfortA., MichelV., ThirionB., GriselO., BlondelM., PrettenhoferP., WeissR., DubourgV., VanderplasJ., PassosA., CournapeauD., BrucherM., PerrotM. and DuchesnayE., Scikit-learn: Machine learning in python, Journal of Machine Learning Research12(Oct) (2011), 2825\u20132830.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_23_2","doi-asserted-by":"crossref","unstructured":"PenningtonJ. SocherR. and ManningC. Glove: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) pp. 1532\u20131543 Doha Qatar Oct. 2014. Association for Computational Linguistics.","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","unstructured":"PersingI. and NgV. End-to-end argumentation mining in student essays. In Proceedings of NAACL-HLT (2016) pp. 1384\u20131394.","DOI":"10.18653\/v1\/N16-1164"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2018.04.002"},{"key":"e_1_3_2_26_2","doi-asserted-by":"crossref","unstructured":"StabC. and GurevychI. Identifying argumentative discourse structures in persuasive essays. In Procs. of the Conference on Empirical Methods in Natural Language Processing pp. 46\u201356. ACL October 2014.","DOI":"10.3115\/v1\/D14-1006"},{"key":"e_1_3_2_27_2","doi-asserted-by":"crossref","unstructured":"StabC. and GurevychI. Parsing argumentation structures in persuasive essays. Computational Linguistics 2017.","DOI":"10.1162\/COLI_a_00295"},{"key":"e_1_3_2_28_2","doi-asserted-by":"crossref","unstructured":"ToulminS.E. The uses of argument. Cambridge University Press 2003.","DOI":"10.1017\/CBO9780511840005"},{"key":"e_1_3_2_29_2","doi-asserted-by":"crossref","unstructured":"WaltonD. Fundamentals of critical argumentation. Cambridge University Press 2005.","DOI":"10.1017\/CBO9780511807039"},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","unstructured":"WaltonD. ReedC. and Macagno.F. Argumentation schemes. Cambridge University Press 2008.","DOI":"10.1017\/CBO9780511802034"},{"key":"e_1_3_2_31_2","doi-asserted-by":"crossref","unstructured":"WynerA. and Bench-CaponT. Towards an extensible argumentation system. In Proceedings of the Ninth European Conferences on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (2007) pp. 283\u2013294. Springer.","DOI":"10.1007\/978-3-540-75256-1_27"},{"key":"e_1_3_2_32_2","doi-asserted-by":"crossref","unstructured":"WynerA. Mochales-PalauR. MoensM.-F. and MilwardD. Approaches to Text Mining Arguments from Legal Cases (2010) pp. 60\u201379. Springer.","DOI":"10.1007\/978-3-642-12837-0_4"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179874","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-179874","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-179874","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T13:19:34Z","timestamp":1770124774000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-179874"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,22]]},"references-count":31,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2020,8,31]]}},"alternative-id":["10.3233\/JIFS-179874"],"URL":"https:\/\/doi.org\/10.3233\/jifs-179874","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,22]]}}}