Position-aware stepwise tagging method for triples extraction of entity-relationship

Article

Wang, Yuan, Shi, Kaize and Niu, Zhendong. 2021. "Position-aware stepwise tagging method for triples extraction of entity-relationship." Data Analysis and Knowledge Discovery. 5 (10), pp. 71-80. https://doi.org/10.11925/infotech.2096-3467.2021.0302
Article Title

Position-aware stepwise tagging method for triples extraction of entity-relationship

Article CategoryArticle
AuthorsWang, Yuan, Shi, Kaize and Niu, Zhendong
Journal TitleData Analysis and Knowledge Discovery
Journal Citation5 (10), pp. 71-80
Number of Pages10
Year2021
PublisherChinese Academy of Sciences
Place of PublicationChina
ISSN2096-3467
Digital Object Identifier (DOI)https://doi.org/10.11925/infotech.2096-3467.2021.0302
Abstract

[Objective] This paper designs a joint model for overlapping scenes, aiming to effectively extract triples from unstructured texts. [Methods] We designed a tagging method with position-aware stepwise technique. First, the main entities were determined by tagging their start and end positions. Then, we tagged the corresponding objects under each predefined relations. We also added multiple position-aware information to the tagging procedures. Finally, we shared the encoded sequences with the pre-order results and the attention mechanism. [Results] We examined our new model with DuIE, a Chinese public dataset. The performance of our method is better than those of the baseline models, with an F1 value of 0.886. We also verified the effectiveness of the model’s components through ablation studies. [Limitations] More research is needed to investigate the occasionally nested entities. [Conclusions] The proposed method could effectively address the issues facing triple extraction for overlapping scenes, and provide reference for future studies.

KeywordsJoint extraction; Position-aware; Stepwise tagging method
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20204602. Artificial intelligence
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Byline AffiliationsBeijing Institute of Technology, China
University of Technology Sydney
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