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

Guggilla, Chinnappa and Miller, Tristan and Gurevych, Iryna :
CNN- and LSTM-based Claim Classification in Online User Comments.
[Online-Edition: http://aclweb.org/anthology/C/C16/C16-1258.pdf]
Proceedings of the 26th International Conference on Computational Linguistics (COLING)
[Conference or Workshop Item] , (2016)

Official URL: http://aclweb.org/anthology/C/C16/C16-1258.pdf

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 claims 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.

Item Type: Conference or Workshop Item
Erschienen: 2016
Creators: Guggilla, Chinnappa and Miller, Tristan and Gurevych, Iryna
Title: CNN- and LSTM-based Claim Classification in Online User Comments
Language: English
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 claims 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.

Title of Book: Proceedings of the 26th International Conference on Computational Linguistics (COLING)
Uncontrolled Keywords: UKP_reviewed
Divisions: Department of Computer Science
Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Location: Osaka, Japan
Date Deposited: 31 Dec 2016 14:29
Official URL: http://aclweb.org/anthology/C/C16/C16-1258.pdf
Identification Number: TUD-CS-2016-1447
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