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
- Editors:
- Ivan Habernal, Iryna Gurevych, Kevin Ashley, Claire Cardie, Nancy Green, Diane Litman, Georgios Petasis, Chris Reed, Noam Slonim, Vern Walker
- Venue:
- ArgMining
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 91–96
- Language:
- URL:
- https://aclanthology.org/W17-5112
- DOI:
- 10.18653/v1/W17-5112
- 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., ArgMining 2017)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W17-5112.pdf