Research Area:  Machine Learning

Abstract:

This paper offers a comparative analysis of the performance of different supervised machine learning methods and feature sets on argument mining tasks. Specifically, we address the tasks of extracting argumentative segments from texts and predicting the structure between those segments. Eight classifiers and different combinations of six feature types reported in previous work are evaluated. The results indicate that overall best performing features are the structural ones. Although the performance of classifiers varies depending on the feature combinations and corpora used for training and testing, Random Forest seems to be among the best performing classifiers. These results build a basis for further development of argument mining techniques and can guide an implementation of argument mining into different applications such as argument based search.

Keywords:  

Author(s) Name:  Ahmet Aker, Alfred Sliwa, Yuan Ma, Ruishen Lui, Niravkumar Borad, Seyedeh Ziyaei, Mina Ghobadi

Publisher name:  Association for Computational Linguistics

DOI:  10.18653/v1/W17-5112

Volume Information:  

Paper Link:   https://aclanthology.org/W17-5112/