Please use this identifier to cite or link to this item: https://doi.org/10.1109/IJCNN.2012.6252548
Title: Connective prediction using machine learning for implicit discourse relation classification
Authors: Xu, Y.
Lan, M.
Lu, Y.
Niu, Z.Y.
Tan, C.L. 
Issue Date: 2012
Source: Xu, Y.,Lan, M.,Lu, Y.,Niu, Z.Y.,Tan, C.L. (2012). Connective prediction using machine learning for implicit discourse relation classification. Proceedings of the International Joint Conference on Neural Networks. ScholarBank@NUS Repository. https://doi.org/10.1109/IJCNN.2012.6252548
Abstract: Implicit discourse relation classification is a challenge task due to missing discourse connective. Some work directly adopted machine learning algorithms and linguistically informed features to address this task. However, one interesting solution is to automatically predict implicit discourse connective. In this paper, we present a novel two-step machine learning-based approach to implicit discourse relation classification. We first use machine learning method to automatically predict the discourse connective that can best express the implicit discourse relation. Then the predicted implicit discourse connective is used to classify the implicit discourse relation. Experiments on Penn Discourse Treebank 2.0 (PDTB) and Biomedical Discourse Relation Bank (BioDRB) show that our method performs better than the baseline system and previous work. © 2012 IEEE.
Source Title: Proceedings of the International Joint Conference on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/41557
ISBN: 9781467314909
DOI: 10.1109/IJCNN.2012.6252548
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