Abstract
Given a source document with extracted mentions, entity linking calls for mapping the mention to an entity in reference knowledge base. Previous entity linking approaches mainly focus on generic statistic features to link mentions independently. However, additional interdependence among mentions in the same document achieved from relational analysis can improve the accuracy. This paper propose a collective entity linking model which effectively leverages the global interdependence among mentions in the same source document. The model unifies semantic relations and co-reference relations into relational inference for semantic information extraction. Graph based linking algorithm is utilized to ensure per mention with only one candidate entity. Experiments on datasets show the proposed model significantly out-performs the state-of-the-art relatedness approaches in term of accuracy.
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The research of this paper is partially supported by National 863 project 2015AA015404 and open project of State key lab. Smart manufacturing for special vehicles and transmission system.
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Gong, J., Feng, C., Liu, Y., Shi, G., Huang, H. (2017). Collective Entity Linking on Relational Graph Model with Mentions. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_14
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DOI: https://doi.org/10.1007/978-3-319-69005-6_14
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