Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (5): 54-65    DOI: 10.11925/infotech.2096-3467.2019.1006
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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.

Received: 05 September 2019      Published: 15 June 2020
 ZTFLH: TP393 G35
Corresponding Authors: Zhao Zhongying     E-mail: zzysuin@163.com
 The Framework of DepREm-CRF The Notations and Descriptions The Semantic Representation of Words An Example of Part-of-Speech Tagging An Example of Lemmatization An Example of BIO Training Sets and Testing Sets for DepREm-CRF The Results of DepREm-CRF with Different Semantic Information An Example of Term Extraction with Different Semantic Information Comparison of DepREm-CRF with Other Competitive Models ($F1$:%) The Accuracy of Term Extraction with Different Typed and Scaled Auxiliary Corpora