@inproceedings{tianran-etal-2021-improving,
title = "Improving Entity Linking by Encoding Type Information into Entity Embeddings",
author = "Tianran, Li and
Erguang, Yang and
Yujie, Zhang and
Yufeng, Chen and
Jinan, Xu",
booktitle = "Proceedings of the 20th Chinese National Conference on Computational Linguistics",
month = aug,
year = "2021",
address = "Huhhot, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2021.ccl-1.97",
pages = "1087--1095",
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.{''}",
language = "English",
}
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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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%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
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 aug
%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)
ACL