Unit Segmentation of Argumentative Texts

authored by

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

Abstract

The segmentation of an argumentative text into argument units and their nonargumentative 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.

External Organisation(s)

Bauhaus-Universität Weimar

Type

Conference contribution

Pages

118-128

No. of pages

11

Publication date

09.2017

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Computational Theory and Mathematics, Information Systems, Computer Science Applications

Electronic version(s)

https://doi.org/10.18653/v1/W17-5115 (Access: Open)