基于依存关系嵌入与条件随机场的商品属性抽取方法

Extracting Product Properties with Dependency Relationship Embedding and Conditional Random Field

【目的】基于依存关系嵌入设计多种单词表示,获取单词的潜在语义特征,提高条件随机场对评论中商品属性的抽取能力。【方法】提出一种基于依存关系嵌入与条件随机场的商品属性抽取方法。基于单词属性、单词依存关系及其词嵌入形式构建三类单词语义信息,包括:基本语义信息、结构语义信息和类别语义信息;结合三类语义信息与条件随机场模型抽取商品的属性。【结果】与不加入语义信息相比,融合三类语义信息的方法在准确率上提高3.97%;与已有的代表性模型相比,本文方法在F1值上最多提高7.65%。【局限】情感词和属性关系紧密,未对评论中属性和情感词之间的关系进行深入挖掘。【结论】本文方法能够有效地抽取商品评论数据的属性,为基于属性的细粒度情感分析奠定良好的基础。

[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 was3.97% higher than that of the DepREm-CRF. Its F1 valuewas 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.