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Extracting Product Properties with Dependency Relationship Embedding and Conditional Random Field |
Li Chengliang,Zhao Zhongying(),Li Chao,Qi Liang,Wen Yan |
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China |
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Abstract [Objective] This paper designs multiple word representation methods, aiming to obtain the latent semantic features and extract product properties from reviews.[Methods] First, we used word properties, dependency relationship and embedding techniques to construct three types of word representations, which included basic, structural and category semantic information. Then, we applied conditional random field model to extract product properties with these semantic information.[Results] The accuracy of the proposed method was 3.97% higher than that of the DepREm-CRF.Its F1 value was up to 7.65% better than the popular ones.[Limitations] More research is needed to investigate the relationship between online sentiments and properties.[Conclusions] The proposed method is able to effectively extract properties from product reviews, and lays good foundation for fine-grained sentiment analysis research.
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Received: 05 September 2019
Published: 15 June 2020
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Corresponding Authors:
Zhao Zhongying
E-mail: zzysuin@163.com
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