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        <title>Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction</title>
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    <name type="personal">
        <namePart type="given">Boer</namePart>
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    <name type="personal">
        <namePart type="given">Lu</namePart>
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    <name type="personal">
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        <namePart type="family">Yu</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <namePart type="family">Yih</namePart>
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    <abstract>Sememes are defined as the atomic units to describe the semantic meaning of concepts. Due to the difficulty of manually annotating sememes and the inconsistency of annotations between experts, the lexical sememe prediction task has been proposed. However, previous methods heavily rely on word or character embeddings, and ignore the fine-grained information. In this paper, we propose a novel pre-training method which is designed to better incorporate the internal information of Chinese character. The Glyph enhanced Chinese Character representation (GCC) is used to assist sememe prediction. We experiment and evaluate our model on HowNet, which is a famous sememe knowledge base. The experimental results show that our method outperforms existing non-external information models.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.386</identifier>
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        <date>2021-11</date>
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            <start>4549</start>
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%0 Conference Proceedings
%T Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction
%A Lyu, Boer
%A Chen, Lu
%A Yu, Kai
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F lyu-etal-2021-glyph-enhanced
%X Sememes are defined as the atomic units to describe the semantic meaning of concepts. Due to the difficulty of manually annotating sememes and the inconsistency of annotations between experts, the lexical sememe prediction task has been proposed. However, previous methods heavily rely on word or character embeddings, and ignore the fine-grained information. In this paper, we propose a novel pre-training method which is designed to better incorporate the internal information of Chinese character. The Glyph enhanced Chinese Character representation (GCC) is used to assist sememe prediction. We experiment and evaluate our model on HowNet, which is a famous sememe knowledge base. The experimental results show that our method outperforms existing non-external information models.
%R 10.18653/v1/2021.findings-emnlp.386
%U https://aclanthology.org/2021.findings-emnlp.386
%U https://doi.org/10.18653/v1/2021.findings-emnlp.386
%P 4549-4555

Markdown (Informal)

[Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction](https://aclanthology.org/2021.findings-emnlp.386) (Lyu et al., Findings 2021)

• Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction (Lyu et al., Findings 2021)

ACL

• Boer Lyu, Lu Chen, and Kai Yu. 2021. Glyph Enhanced Chinese Character Pre-Training for Lexical Sememe Prediction. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4549–4555, Punta Cana, Dominican Republic. Association for Computational Linguistics.