Proceedings of the 2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017)

Argument Component Classification with Context-LSTM

Authors
Chaoming Wang, Xudong Chen
Corresponding Author
Chaoming Wang
Available Online June 2017.
DOI
https://doi.org/10.2991/gcmce-17.2017.23How to use a DOI?
Keywords
Argumentation Mining, Argument Component Classification, LSTM
Abstract
Argumentation Mining (AM) is an emerging field of natural language understanding. It extends sentiment analysis, topic modeling and other existing text mining methods, aimed at tap the latent logical relationship between sentences. At present, Argument Component Classification (ACC) is a challenging subtask in AM. Existing popular SVM based models heavily rely on artificially constructed and domain dependent features, and they cannot nicely model the context relations among sentences. In this paper, we propose an ACC method based on Context-LSTM, which do not require any artificial features. Moreover, Context-LSTM can model the contextual information of the current sentence very well. We conduct experiments on two well-annotated corpora and get state-of-art results both.
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Proceedings
2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-383-8
ISSN
2352-5401
DOI
https://doi.org/10.2991/gcmce-17.2017.23How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Chaoming Wang
AU  - Xudong Chen
PY  - 2017/06
DA  - 2017/06
TI  - Argument Component Classification with Context-LSTM
BT  - 2017 Global Conference on Mechanics and Civil Engineering (GCMCE 2017)
PB  - Atlantis Press
SP  - 115
EP  - 121
SN  - 2352-5401
UR  - https://doi.org/10.2991/gcmce-17.2017.23
DO  - https://doi.org/10.2991/gcmce-17.2017.23
ID  - Wang2017/06
ER  -