CNN- and LSTM-based Claim Classification in Online User Comments

Chinnappa Guggilla, Tristan Miller, Iryna Gurevych


Abstract
When processing arguments in online user interactive discourse, it is often necessary to determine their bases of support. In this paper, we describe a supervised approach, based on deep neural networks, for classifying the claims made in online arguments. We conduct experiments using convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) on two claim data sets compiled from online user comments. Using different types of distributional word embeddings, but without incorporating any rich, expensive set of features, we achieve a significant improvement over the state of the art for one data set (which categorizes arguments as factual vs. emotional), and performance comparable to the state of the art on the other data set (which categorizes propositions according to their verifiability). Our approach has the advantages of using a generalized, simple, and effective methodology that works for claim categorization on different data sets and tasks.
Anthology ID:
C16-1258
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
2740–2751
Language:
URL:
https://aclanthology.org/C16-1258
DOI:
Bibkey:
Cite (ACL):
Chinnappa Guggilla, Tristan Miller, and Iryna Gurevych. 2016. CNN- and LSTM-based Claim Classification in Online User Comments. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2740–2751, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
CNN- and LSTM-based Claim Classification in Online User Comments (Guggilla et al., COLING 2016)
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PDF:
https://aclanthology.org/C16-1258.pdf