Unit Segmentation of Argumentative Texts

Yamen Ajjour, Wei-Fan Chen, Johannes Kiesel, Henning Wachsmuth, Benno Stein

Abstract
The segmentation of an argumentative text into argument units and their non-argumentative counterparts is the first step in identifying the argumentative structure of the text. Despite its importance for argument mining, unit segmentation has been approached only sporadically so far. This paper studies the major parameters of unit segmentation systematically. We explore the effectiveness of various features, when capturing words separately, along with their neighbors, or even along with the entire text. Each such context is reflected by one machine learning model that we evaluate within and across three domains of texts. Among the models, our new deep learning approach capturing the entire text turns out best within all domains, with an F-score of up to 88.54. While structural features generalize best across domains, the domain transfer remains hard, which points to major challenges of unit segmentation.
Anthology ID:
W17-5115
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:
118–128
Language:
URL:
https://aclanthology.org/W17-5115
DOI:
10.18653/v1/W17-5115
Bibkey:
Cite (ACL):
Yamen Ajjour, Wei-Fan Chen, Johannes Kiesel, Henning Wachsmuth, and Benno Stein. 2017. Unit Segmentation of Argumentative Texts. In Proceedings of the 4th Workshop on Argument Mining, pages 118–128, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
Unit Segmentation of Argumentative Texts (Ajjour et al., ArgMining 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/dois-2013-emnlp/W17-5115.pdf
Code
 webis-de/unit-segmentation-of-argumentative-texts
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