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        <title>Entity Linking via Joint Encoding of Types, Descriptions, and Context</title>
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    <abstract>For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively “embed” entities that are new to the KB, and is able to link its mentions accurately.</abstract>
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%0 Conference Proceedings
%T Entity Linking via Joint Encoding of Types, Descriptions, and Context
%A Gupta, Nitish
%A Singh, Sameer
%A Roth, Dan
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F gupta-etal-2017-entity
%X For accurate entity linking, we need to capture various information aspects of an entity, such as its description in a KB, contexts in which it is mentioned, and structured knowledge. Additionally, a linking system should work on texts from different domains without requiring domain-specific training data or hand-engineered features. In this work we present a neural, modular entity linking system that learns a unified dense representation for each entity using multiple sources of information, such as its description, contexts around its mentions, and its fine-grained types. We show that the resulting entity linking system is effective at combining these sources, and performs competitively, sometimes out-performing current state-of-the-art systems across datasets, without requiring any domain-specific training data or hand-engineered features. We also show that our model can effectively “embed” entities that are new to the KB, and is able to link its mentions accurately.
%R 10.18653/v1/D17-1284
%U https://aclanthology.org/D17-1284/
%U https://doi.org/10.18653/v1/D17-1284
%P 2681-2690

Markdown (Informal)

[Entity Linking via Joint Encoding of Types, Descriptions, and Context](https://aclanthology.org/D17-1284/) (Gupta et al., EMNLP 2017)

• Entity Linking via Joint Encoding of Types, Descriptions, and Context (Gupta et al., EMNLP 2017)

ACL

• Nitish Gupta, Sameer Singh, and Dan Roth. 2017. Entity Linking via Joint Encoding of Types, Descriptions, and Context. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2681–2690, Copenhagen, Denmark. Association for Computational Linguistics.