Combining Tree Kernels and Tree Representations to Classify Argumentative Stances

This work investigates how the combination of different tree representations with different Tree Kernel functions influences the results of the classifications in two specific case studies. One case study is related to the classification of argumentative stances of support, the other one is related to the classification of stances of opposition. Results show that some Tree Kernels achieves not only higher results but also a higher level of generalization. Moreover, it seems that also the kind of tree representation influences the performances of classifiers. In this study, we thus explore this relation between tree representation and different Tree Kernels, considering also compositional trees.

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

[2]  Serena Villata,et al.  Argument Mining on Clinical Trials , 2018, COMMA.

[3]  Douglas Walton,et al.  The Basic Slippery Slope Argument , 2015 .

[4]  Roberto Basili,et al.  Semantic Compositionality in Tree Kernels , 2014, CIKM.

[5]  Matthias Hagen,et al.  A News Editorial Corpus for Mining Argumentation Strategies , 2016, COLING.

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

[7]  Roberto Basili,et al.  Semantic convolution kernels over dependency trees: smoothed partial tree kernel , 2011, CIKM '11.

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

[9]  Paolo Torroni,et al.  Context-Independent Claim Detection for Argument Mining , 2015, IJCAI.

[10]  Paolo Torroni,et al.  CLAUDETTE: an automated detector of potentially unfair clauses in online terms of service , 2018, Artificial Intelligence and Law.

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

[12]  Chris Reed,et al.  An Online Annotation Assistant for Argument Schemes , 2019, LAW@ACL.

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

[14]  Michael Collins,et al.  Convolution Kernels for Natural Language , 2001, NIPS.

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

[16]  Alexander J. Smola,et al.  Fast Kernels for String and Tree Matching , 2002, NIPS.

[17]  Roberto Basili,et al.  Towards Compositional Tree Kernels , 2013, JSSP.

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

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

[20]  Noam Slonim,et al.  A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics , 2014, ArgMining@ACL.