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        <title>Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding</title>
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    <name type="personal">
        <namePart type="given">Yixin</namePart>
        <namePart type="family">Cao</namePart>
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        <namePart type="given">Lifu</namePart>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Juanzi</namePart>
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    <abstract>Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative and quantitative analysis demonstrate the high quality of the word, entity and multi-prototype mention embeddings. Using entity linking as a study case, we apply our disambiguation method as well as the multi-prototype mention embeddings on the benchmark dataset, and achieve state-of-the-art performance.</abstract>
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    <identifier type="doi">10.18653/v1/P17-1149</identifier>
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%0 Conference Proceedings
%T Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding
%A Cao, Yixin
%A Huang, Lifu
%A Ji, Heng
%A Chen, Xu
%A Li, Juanzi
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F cao-etal-2017-bridge
%X Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative and quantitative analysis demonstrate the high quality of the word, entity and multi-prototype mention embeddings. Using entity linking as a study case, we apply our disambiguation method as well as the multi-prototype mention embeddings on the benchmark dataset, and achieve state-of-the-art performance.
%R 10.18653/v1/P17-1149
%U https://aclanthology.org/P17-1149
%U https://doi.org/10.18653/v1/P17-1149
%P 1623-1633

Markdown (Informal)

[Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding](https://aclanthology.org/P17-1149) (Cao et al., ACL 2017)

• Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding (Cao et al., ACL 2017)

ACL

• Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, and Juanzi Li. 2017. Bridge Text and Knowledge by Learning Multi-Prototype Entity Mention Embedding. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1623–1633, Vancouver, Canada. Association for Computational Linguistics.