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    <titleInfo>
        <title>Improving Entity Linking by Encoding Type Information into Entity Embeddings</title>
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    <name type="personal">
        <namePart type="given">Li</namePart>
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            <title>Proceedings of the 20th Chinese National Conference on Computational Linguistics</title>
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            <namePart type="given">Sheng</namePart>
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            <namePart type="given">Maosong</namePart>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>Entity Linking (EL) refers to the task of linking entity mentions in the text to the correct entities inthe Knowledge Base (KB) in which entity embeddings play a vital and challenging role because of the subtle differences between entities. However existing pre-trained entity embeddings onlylearn the underlying semantic information in texts yet the fine-grained entity type informationis ignored which causes the type of the linked entity is incompatible with the mention context. In order to solve this problem we propose to encode fine-grained type information into entity embeddings. We firstly pre-train word vectors to inject type information by embedding wordsand fine-grained entity types into the same vector space. Then we retrain entity embeddings withword vectors containing fine-grained type information. By applying our entity embeddings to twoexisting EL models our method respectively achieves 0.82% and 0.42% improvement on average F1 score of the test sets. Meanwhile our method is model-irrelevant which means it can helpother EL models.</abstract>
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        <url>https://aclanthology.org/2021.ccl-1.97</url>
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    <part>
        <date>2021-08</date>
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            <start>1087</start>
            <end>1095</end>
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%0 Conference Proceedings
%T Improving Entity Linking by Encoding Type Information into Entity Embeddings
%A Tianran, Li
%A Erguang, Yang
%A Yujie, Zhang
%A Yufeng, Chen
%A Jinan, Xu
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G English
%F tianran-etal-2021-improving
%X Entity Linking (EL) refers to the task of linking entity mentions in the text to the correct entities inthe Knowledge Base (KB) in which entity embeddings play a vital and challenging role because of the subtle differences between entities. However existing pre-trained entity embeddings onlylearn the underlying semantic information in texts yet the fine-grained entity type informationis ignored which causes the type of the linked entity is incompatible with the mention context. In order to solve this problem we propose to encode fine-grained type information into entity embeddings. We firstly pre-train word vectors to inject type information by embedding wordsand fine-grained entity types into the same vector space. Then we retrain entity embeddings withword vectors containing fine-grained type information. By applying our entity embeddings to twoexisting EL models our method respectively achieves 0.82% and 0.42% improvement on average F1 score of the test sets. Meanwhile our method is model-irrelevant which means it can helpother EL models.
%U https://aclanthology.org/2021.ccl-1.97
%P 1087-1095

Markdown (Informal)

[Improving Entity Linking by Encoding Type Information into Entity Embeddings](https://aclanthology.org/2021.ccl-1.97) (Tianran et al., CCL 2021)

• Improving Entity Linking by Encoding Type Information into Entity Embeddings (Tianran et al., CCL 2021)

ACL

• Li Tianran, Yang Erguang, Zhang Yujie, Chen Yufeng, and Xu Jinan. 2021. Improving Entity Linking by Encoding Type Information into Entity Embeddings. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 1087–1095, Huhhot, China. Chinese Information Processing Society of China.