Extracting Argument and Domain Words for Identifying Argument Components in Texts
Huy Nguyen and Diane Litman
2nd Workshop on Argumentation Mining (ARG-MINING 2015)
Denver, Colorado, USA, June 4, 2015
Argument mining studies in natural language text often use lexical (e.g. n-grams) and syntactic (e.g. grammatical production rules) features with all possible values. In prior work on a corpus of academic essays, we demonstrated that such large and sparse feature spaces can cause difficulty for feature selection and proposed a method to design a more compact feature space. The proposed feature design is based on post-processing a topic model to extract argument and domain words. In this paper we investigate the generality of this approach, by applying our methodology to a new corpus of persuasive essays. Our experiments show that replacing n-grams and syntactic rules with features and constraints using extracted argument and domain words significantly improves argument mining performance for persuasive essays.
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