Argumentation in academic writing is a challenging task required to communicate clear ideas. Exposed ideas have to be supported by reasoned arguments. Arguments are composed of components such as premises and conclusions. In this paper, we present an approach to classify argumentative components using language models and machine learning algorithms on a new corpus of academic theses and research proposals. We explore the use of lexical, syntactic, semantic and indicator features to tackle this task. We found that lexical features provide the best efficacy for the classification. For language models, the best features were syntactical. But our experiments showed that a document occurrence representation with unigrams achieved the best accuracy. We also tested the conclusions about the representation and classifier on theses according to their study level (undergraduate, master, and doctoral). We analyzed the information gain of features and found patterns that are part of argumentative markers.
Al-RfouR., PerozziB. and SkienaS., Polyglot: Distributed word representations for multilingual nlp, In Proceedings of the Seventeenth Conference on Computational Natural Language Learning , Sofia, Bulgaria. Association for Computational Linguistics (2013), 183–192.
2.
BirdS., KleinE. and LoperE., Natural Language Processing with Python. O’Reilly Media, Inc., 1, 2009.
3.
BrizA., PonsS. and PortolésJ., Diccionario de partículas discursivas del español. In El diccionario como puente entre las lenguas y culturas del mundo. Actas del II Congreso Internacional de Lexicografía Hispánica, Alicante, Biblioteca Virtual Cervantes, Biblioteca Virtual Miguel de Cervantes, Alicante, (2008), 217–227.
4.
CabreraJ.M., EscalanteH.J. and Montes-yM., Gómez, Distributional term representations for short-text categorization, In International Conference on Computational Linguistics and Intelligent Text Processing (2013), 335–346. Springer.
5.
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 ’12 , Association for Computational Linguistics (2012), 208–212.
6.
CapaldiN., Cómo ganar una discusiónGedisa, 1990.
7.
CarstensL. and ToniF., Towards relation based argumentation mining, In Proceedings of the 2ndWorkshop on Argumentation MiningDenver, CO, Association for Computational Linguistics, (2015), 29–34.
8.
DaxenbergerJ., EgerS., HabernalI., StabC. and GurevychI., What is the essence of a claim? cross-domain claim identification, Association for Computational Linguistics, In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (2017), 2045–2056.
9.
FierroC., FuentesC., PérezJ. and QuezadaM., 200k+ crowdsourced political arguments for a new chilean constitution, In Proceedings of the 4th Workshop on Argument Mining , Copenhagen, Denmark (2017) 1–10. Association for Computational Linguistics.
10.
FreemanJ.B., Argument Structure: Representation and Theory, Springer2011.
11.
García-GorrostietaJ.M., López-LópezA. and González-LópezS., Towards automatic assessment of argumentation in theses justifications. In LavouéÉ., DrachslerH., VerbertK., BroisinJ. and Pérez-SanagustínM., editors, Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced LearningEC-TEL Tallinn, Estonia, Proceedings, Springer International Publishing, (2017), 54–66.
12.
González-LópezS. and López-LópezA., Colección de tesis y propuesta de investigación en tics: Un recurso para su análisis y estudio,n Educativa, In XIII Congreso Nacional de Investigaci’o (2015), 1–15.
13.
GorrostietaJ.M.G. and López-LópezA., Argumentation identification for academic support in undergraduate writings. In VerbertK., SharplesM. and KlobučarT., editors, Adaptive and Adaptable Learning: 11th European Conference on Technology Enhanced LearningEC-TEL Lyon, France, Proceedings, Springer International Publishing (2016), 98–109.
14.
GoudasT., LouizosC., PetasisG. and KarkaletsisV., Argument extraction from news, blogs, and social media, In Hellenic Conference on Artificial Intelligence (2014) 287–299. Springer.
15.
GreenN., Identifying argumentation schemes in genetics research articles, Association for Computational Linguistics, In Proceedings of the 2nd Workshop on Argumentation Mining (2015), 12–21.
16.
HabernalI. and GurevychI., Argumentation mining in user-generated web discourse, Computational Linguistics43(1) (2017), 125–179.
17.
HallM., FrankE., HolmesG., PfahringerB., ReutemannP. and WittenI.H., The weka data mining software: An update, ACM SIGKDD Explorations Newsletter11(1) (2009), 10–18.
18.
JurafskyD. and MartinJ.H., Speech and Language Processing2, Prentice-Hall Inc., Upper Saddle River, NJ, USA, 2009.
19.
KirschnerC., Eckle-KohlerJ. and GurevychI., Linking the thoughts: Analysis of argumentation structures in scientific publications, Association for Computational Linguistics, In Proceedings of the 2nd Workshop on Argumentation Mining (2015), 1–11.
20.
LandisJ.R. and KochG.G., The measurement of observer agreement for categorical data, Biometrics33(1) (1977), 159–174.
López FerreroC. and GarcíaM., Negroni La argumentación en los géneros académicos,In Actas del Congreso Internacional La Argumentaci’onUniversidad de Buenos Aires (2003), 1121–1129.
23.
MochalesR. and MoensM.-F., Study on the structure of argumentation in case law, In Proceedings of the 2008 Conference on Legal Knowledge and Information Systems , IOS Press (2008), 11–20.
24.
MochalesR. and MoensM.-F., Argumentation mining, Artificial Intelligence and Law19(1) (2011), 1–22.
25.
MoensM.-F., BoiyE., PalauR.M. and ReedC., Automatic detection of arguments in legal texts, ACM, In Proceedings of the 11th International Conference on Artificial Intelligence and Law (2007), 225–230.
26.
NguyenH. and LitmanD., Extracting argument and domain words for identifying argument components in texts, Association for Computational Linguistics, In Proceedings of the 2nd Workshop on Argumentation Mining (2015), 22–28.
27.
PadróL. and StanilovskyE., Freeling 3.0: Towards wider multilinguality, Istanbul, Turkey, European Language Resources Association (ELRA), In Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12) (2012), 2473–2479.
28.
PedregosaF., VaroquauxG., GramfortA., MichelV., ThirionB., GriselO., BlondelM., PrettenhoferP., WeissR., DubourgV., VanderplasJ., PassosA. and CournapeauD., BrucherM., PerrotM. and DuchesnayE., Scikit-learn: Machine learning in python, Journal of Machine Learning Research12(Oct) (2011), 2825–2830.
29.
PersingI. and NgV., Modeling argument strength in student essays, Beijing, China, Association for Computational Linguistics, In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (2015), 543–552.
30.
RodríguezC.F., Diccionario de conectores y operadores del español, Arco/Libros, 2009.
31.
SánchezC., Avendaño, Los conectores discursivos: Su empleo en redacciones de estudiantes universitarios costarricenses,a y Lingüística de la Universidad de Costa Rica, Revista de Filolog’ı31(2) (2005).
32.
SardianosC., KatakisI.M., PetasisG. and KarkaletsisV., Argument extraction from news, Association for Computational Linguistics, In Proceedings of the 2nd Workshop on Argumentation Mining (2015), 56–66.
33.
StabC. and GurevychI., Identifying argumentative discourse structures in persuasive essays, Association for Computational Linguistics, In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2014), 46–56.
34.
StabC.M.E., Argumentative Writing Support by means of Natural Language Processing, PhD thesis, Technische Universität Darmstadt, 2017.
35.
StolckeA., Srilm – an extensible language modeling toolkit, In Proceedings of the 7TH International Conference on Spoken Language Processing (2002), 901–904.
36.
ToulminS.E., The uses of argument, Cambridge University Press2003.
37.
VillalbaM.P.G. and Saint-DizierP., Some facets of argument mining for opinion analysis, In Proceedings of the 2012 International Conference on Computational Models of Argument (2012), 23–34.
38.
WaltonD., Fundamentals of critical argumentation, Cambridge University Press, 2005.
39.
WaltonD., ReedC. and MacagnoF., Argumentation schemes, Cambridge University Press2008.
40.
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) 283–294. Springer.
41.
WynerA., Mochales-PalauR., MoensM.-F. and MilwardD., Approaches to Text Mining Arguments from Legal Cases, Springer, 2010, pp. 60–79 .
