Classifying argumentative stances of opposition using Tree Kernels

The approach proposed in this study aims to classify argumentative oppositions. A major assumption of this work is that discriminating among different argumentative stances of support and opposition can facilitate the detection of Argument Schemes. While using Tree Kernels for classification problems can be useful in many Argument Mining sub-tasks, this work focuses on the classification of opposition stances. We show that Tree Kernels can be successfully used (alone or in combination with traditional textual vectorizations) to discriminate between different stances of opposition without requiring highly engineered features. Moreover, this study compare the results of Tree Kernels classifiers with the results of classifiers which use traditional features such as TFIDF and n-grams. This comparison shows that Tree Kernel classifiers can outperform TFIDF and n-grams classifiers.

[1]  Roberto Basili,et al.  Tree Kernels for Semantic Role Labeling , 2008, CL.

[2]  Alessandro Moschitti,et al.  Learning pairwise patterns in Community Question Answering , 2019, Intelligenza Artificiale.

[3]  Roberto Basili,et al.  Structured Lexical Similarity via Convolution Kernels on Dependency Trees , 2011, EMNLP.

[4]  Chris Reed,et al.  Argumentation Schemes , 2008 .

[5]  Davide Liga,et al.  Argumentative Evidences Classification and Argument Scheme Detection Using Tree Kernels , 2019, ArgMining@ACL.

[6]  Paolo Torroni,et al.  Argument Mining: A Machine Learning Perspective , 2015, TAFA.

[7]  Chris Reed,et al.  Argument Mining Using Argumentation Scheme Structures , 2016, COMMA.

[8]  Alessandro Moschitti,et al.  Convolution Kernels on Constituent, Dependency and Sequential Structures for Relation Extraction , 2009, EMNLP.

[9]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[10]  Serena Villata,et al.  Five Years of Argument Mining: a Data-driven Analysis , 2018, IJCAI.

[11]  Iryna Gurevych,et al.  Argumentation Mining on the Web from Information Seeking Perspective , 2014, ArgNLP.

[12]  Paolo Torroni,et al.  MARGOT: A web server for argumentation mining , 2016, Expert Syst. Appl..

[13]  Alessandro Moschitti,et al.  Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees , 2006, ECML.

[14]  Roberto Basili,et al.  KeLP: a Kernel-based Learning Platform for Natural Language Processing , 2015, ACL.

[15]  Brendan T. O'Connor,et al.  Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics , 2011 .

[16]  Eduard Hovy,et al.  Identifying Metaphorical Word Use with Tree Kernels , 2013 .

[17]  Fiona Browne,et al.  Applying Kernel Methods to Argumentation Mining , 2012, FLAIRS.

[18]  Monica Palmirani,et al.  Detecting "Slippery Slope" and Other Argumentative Stances of Opposition Using Tree Kernels in Monologic Discourse , 2019, RuleML+RR.

[19]  Karin Baier,et al.  The Uses Of Argument , 2016 .