@inproceedings{ajjour-etal-2017-unit,
title = "Unit Segmentation of Argumentative Texts",
author = "Ajjour, Yamen and
Chen, Wei-Fan and
Kiesel, Johannes and
Wachsmuth, Henning and
Stein, Benno",
editor = "Habernal, Ivan and
Gurevych, Iryna and
Ashley, Kevin and
Cardie, Claire and
Green, Nancy and
Litman, Diane and
Petasis, Georgios and
Reed, Chris and
Slonim, Noam and
Walker, Vern",
booktitle = "Proceedings of the 4th Workshop on Argument Mining",
month = sep,
year = "2017",
address = "Copenhagen, Denmark",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-5115",
doi = "10.18653/v1/W17-5115",
pages = "118--128",
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.",
}
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%0 Conference Proceedings
%T Unit Segmentation of Argumentative Texts
%A Ajjour, Yamen
%A Chen, Wei-Fan
%A Kiesel, Johannes
%A Wachsmuth, Henning
%A Stein, Benno
%Y Habernal, Ivan
%Y Gurevych, Iryna
%Y Ashley, Kevin
%Y Cardie, Claire
%Y Green, Nancy
%Y Litman, Diane
%Y Petasis, Georgios
%Y Reed, Chris
%Y Slonim, Noam
%Y Walker, Vern
%S Proceedings of the 4th Workshop on Argument Mining
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F ajjour-etal-2017-unit
%X 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.
%R 10.18653/v1/W17-5115
%U https://aclanthology.org/W17-5115
%U https://doi.org/10.18653/v1/W17-5115
%P 118-128
Markdown (Informal)
[Unit Segmentation of Argumentative Texts](https://aclanthology.org/W17-5115) (Ajjour et al., ArgMining 2017)
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.