Journal
INFORMATION SYSTEMS
Volume 80, Issue -, Pages 56-63Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.is.2018.09.006
Keywords
Opinion extraction; End-to-end; Transition-based system
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Funding
- National Natural Science Foundation of China (NSFC) [61602160, 61672211]
- Natural Science Foundation of Heilongjiang Province (China) [F2016036]
- Special business expenses in Heilongjiang Province (China) [2016-KYYWF-0183]
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Fine-grained opinion extraction has received increasing interests in the natural language processing community. It usually involves several subtasks. Recently, joint methods and neural models have been investigated by several studies, achieving promising performance by using graph-based models such as conditional random field. In this work, we propose a novel end-to-end neural model alternatively for joint opinion extraction, by using a transition-based framework. First, we exploit multi-layer bi-directional long short term memory (LSTM) networks to encode the input sentences, and then decode incrementally based on partial output results dominated by a transition system. We use global normalization and beam search for training and decoding. Experiments on a standard benchmark show that the proposed end-to-end model can achieve competitive results compared with the state-of-the-art neural models of opinion extraction. (C) 2018 Elsevier Ltd. All rights reserved.
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