Extracting Relationship Among Military Domains with BERT and Relation Position Features
Ma Jiangwei1,Lv Xueqiang1,You Xindong1(),Xiao Gang2,Han Junmei2
1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science and Technology University, Beijing 100101, China 2National Key Laboratory of Complex System Simulation, Beijing 100101, China
[Objective] This article addresses the difficulties of relationship extraction due to overlapping entity relationship in military texts. [Methods] We used the BERT model as the encoder for the input texts, and used the hierarchical reinforcement learning approach to decode relationship and their corresponding entities. Then, we merged the relational position features in the entity decoding process to construct a relationship extraction model for military domains. [Results] The F1 value reached 82.2% on the military weapon and equipment dataset, which was about 8% higher than other methods. Using the publicly available NYT10 and NYT10-sub datasets, the F1 values reached 71.8% and 69.0%, which was about 7% and 9% higher than other methods. [Limitations] The new method’s extraction performance is better on manually annotated datasets. More research is needed to improve it performance on remotely supervised datasets with much noise. [Conclusions] The HBP method could effectively extract relationship among the military domains, and has some generalization potentiality.
马江微, 吕学强, 游新冬, 肖刚, 韩君妹. 融合BERT与关系位置特征的军事领域关系抽取方法*[J]. 数据分析与知识发现, 2021, 5(8): 1-12.
Ma Jiangwei, Lv Xueqiang, You Xindong, Xiao Gang, Han Junmei. Extracting Relationship Among Military Domains with BERT and Relation Position Features. Data Analysis and Knowledge Discovery, 2021, 5(8): 1-12.
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