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Entropy 2016, 18(8), 294; doi:10.3390/e18080294

Understanding Gating Operations in Recurrent Neural Networks through Opinion Expression Extraction

School of Computer Science and Technology, Harbin Institute of Technology, No. 92 West Da Zhi Street, Harbin 150001, China
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Academic Editor: Raúl Alcaraz Martínez
Received: 23 March 2016 / Revised: 18 July 2016 / Accepted: 8 August 2016 / Published: 11 August 2016
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Abstract

Extracting opinion expressions from text is an essential task of sentiment analysis, which is usually treated as one of the word-level sequence labeling problems. In such problems, compositional models with multiplicative gating operations provide efficient ways to encode the contexts, as well as to choose critical information. Thus, in this paper, we adopt Long Short-Term Memory (LSTM) recurrent neural networks to address the task of opinion expression extraction and explore the internal mechanisms of the model. The proposed approach is evaluated on the Multi-Perspective Question Answering (MPQA) opinion corpus. The experimental results demonstrate improvement over previous approaches, including the state-of-the-art method based on simple recurrent neural networks. We also provide a novel micro perspective to analyze the run-time processes and gain new insights into the advantages of LSTM selecting the source of information with its flexible connections and multiplicative gating operations. View Full-Text
Keywords: opinion expression extraction; sequence labeling; sentiment analysis; long short-term memory; recurrent neural network opinion expression extraction; sequence labeling; sentiment analysis; long short-term memory; recurrent neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, X.; Liu, Y.; Liu, M.; Sun, C.; Wang, X. Understanding Gating Operations in Recurrent Neural Networks through Opinion Expression Extraction. Entropy 2016, 18, 294.

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