Unit Segmentation of Argumentative Texts

verfasst von

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.

Externe Organisation(en)

Bauhaus-Universität Weimar

Typ

Aufsatz in Konferenzband

Seiten

118-128

Anzahl der Seiten

11

Publikationsdatum

09.2017

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik und Mathematik, Information systems, Angewandte Informatik

Elektronische Version(en)

https://doi.org/10.18653/v1/W17-5115 (Zugang: Offen)