What works and what does not: Classifier and feature analysis for argument mining

Ahmet Aker, Alfred Sliwa, Yuan Ma, Ruishen Lui, Niravkumar Borad, Seyedeh Ziyaei, Mina Ghobadi


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.
Anthology ID:
W17-5112
Volume:
Proceedings of the 4th Workshop on Argument Mining
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venues:
ArgMining | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
91–96
Language:
URL:
https://aclanthology.org/W17-5112
DOI:
10.18653/v1/W17-5112
Bibkey:
Cite (ACL):
Ahmet Aker, Alfred Sliwa, Yuan Ma, Ruishen Lui, Niravkumar Borad, Seyedeh Ziyaei, and Mina Ghobadi. 2017. What works and what does not: Classifier and feature analysis for argument mining. In Proceedings of the 4th Workshop on Argument Mining, pages 91–96, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
What works and what does not: Classifier and feature analysis for argument mining (Aker et al., 2017)
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PDF:
https://aclanthology.org/W17-5112.pdf