Argument Component Classification with Context-LSTM
Chaoming Wang, Xudong Chen
Available Online June 2017.
- https://doi.org/10.2991/gcmce-17.2017.23How to use a DOI?
- Argumentation Mining, Argument Component Classification, LSTM
- 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.
- 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 -