Extracting Entity Relationship with Word Embedding Representation Features
Zhang Qin1,2(), Guo Hongmei1, Zhang Zhixiong1,3
1National Science Library, Chinese Academy of Sciences, Beijing 100190, China 2University of Chinese Academy of Sciences, Beijing 100049, China 3Wuhan Documentation and Information Center, Chinese Academy of Sciences, Wuhan 430071, China
[Objective] This study explores the word embedding representation features for entity relationship extraction, aiming to add semantic message to the existing methods. [Methods] First, we used the feature characteristics at word embedding representation, the vocabulary and the grammar levels to extract relations using Naive Bayesian, Decision Tree and Random Forest models. Then, we obtained the optimal subset of the full features. [Results] The accuracy of the Decision Tree algorithm was 0.48 with full features, which was the best. The F1 score of Member-Collection (E2, E1) was 0.70, and the dependency could help us extract the relations. [Limitations] We need to improve the relation extraction results with small sample size and complex situation. The word vector training method could be further optimized. [Conclusions] This study proves the effectiveness of three types of features. And the word embedding representation level feature plays an important role to extract relations.
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