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Publication . Article . 2019

Aspect Category Detection Based on Attention Mechanism and Bi-Directional LSTM

Chenchao Zhou; Qun Chen; Zhanhuai Li; Bo Zhao; Yongjun Xu; Yang Qin;
Open Access
Chinese
Published: 01 Jun 2019 Journal: Xibei Gongye Daxue Xuebao, volume 37, issue 3, pages 558-564 (issn: 1000-2758, eissn: 2609-7125, Copyright policy )
Publisher: The Northwestern Polytechnical University
Abstract
Online reviews play an increasingly important role in users' purchase decisions. E-commerce websites provide massive user reviews, but it is hard for individuals to make full use of the information. Therefore, it is an urgent task to classify, analyze and summarize the massive comments. In this paper, a model based on attention mechanism and bi-directional long short-term memory (BLSTM) is used to identify the categories of these review objects for the classification of the reviews. The model first uses BLSTM to train the review in the form of word vectors; then according to the part-of-speech, the output vectors of the BLSTM are given corresponding weights. The weights as prior knowledge can guide the learning of attention mechanism to enhance the classification accuracy; finally, the attention mechanism is used to capture category-related important features which are used for category determination. Experiments on the SemEval data set show that our model outperforms the state-of-the-art methods on aspect category detection.
Subjects by Vocabulary

Microsoft Academic Graph classification: Mechanism (biology) Word (computer architecture) SemEval Artificial intelligence business.industry business Computer science Data set Task (project management) Natural language processing computer.software_genre computer

Subjects

user review, aspect category detection, attention mechanism, bi-directional long short-term memory, classification accuracy, Motor vehicles. Aeronautics. Astronautics, TL1-4050, General Engineering

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Digital Humanities and Cultural Heritage
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