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        <title>Put It Back: Entity Typing with Language Model Enhancement</title>
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        <namePart type="given">Hao</namePart>
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    <name type="personal">
        <namePart type="given">Xu</namePart>
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    <name type="personal">
        <namePart type="given">Maosong</namePart>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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    <abstract>Entity typing aims to classify semantic types of an entity mention in a specific context. Most existing models obtain training data using distant supervision, and inevitably suffer from the problem of noisy labels. To address this issue, we propose entity typing with language model enhancement. It utilizes a language model to measure the compatibility between context sentences and labels, and thereby automatically focuses more on context-dependent labels. Experiments on benchmark datasets demonstrate that our method is capable of enhancing the entity typing model with information from the language model, and significantly outperforms the state-of-the-art baseline. Code and data for this paper can be found from https://github.com/thunlp/LME.</abstract>
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%0 Conference Proceedings
%T Put It Back: Entity Typing with Language Model Enhancement
%A Xin, Ji
%A Zhu, Hao
%A Han, Xu
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F xin-etal-2018-put
%X Entity typing aims to classify semantic types of an entity mention in a specific context. Most existing models obtain training data using distant supervision, and inevitably suffer from the problem of noisy labels. To address this issue, we propose entity typing with language model enhancement. It utilizes a language model to measure the compatibility between context sentences and labels, and thereby automatically focuses more on context-dependent labels. Experiments on benchmark datasets demonstrate that our method is capable of enhancing the entity typing model with information from the language model, and significantly outperforms the state-of-the-art baseline. Code and data for this paper can be found from https://github.com/thunlp/LME.
%R 10.18653/v1/D18-1121
%U https://aclanthology.org/D18-1121
%U https://doi.org/10.18653/v1/D18-1121
%P 993-998

Markdown (Informal)

[Put It Back: Entity Typing with Language Model Enhancement](https://aclanthology.org/D18-1121) (Xin et al., EMNLP 2018)

• Put It Back: Entity Typing with Language Model Enhancement (Xin et al., EMNLP 2018)

ACL

• Ji Xin, Hao Zhu, Xu Han, Zhiyuan Liu, and Maosong Sun. 2018. Put It Back: Entity Typing with Language Model Enhancement. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 993–998, Brussels, Belgium. Association for Computational Linguistics.