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        <title>Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph</title>
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        <namePart type="given">Hanyu</namePart>
        <namePart type="family">Duan</namePart>
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    <name type="personal">
        <namePart type="given">Yi</namePart>
        <namePart type="family">Yang</namePart>
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        <namePart type="given">Yan</namePart>
        <namePart type="family">Tam</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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            <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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            <namePart type="given">Lucia</namePart>
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    <abstract>Numeracy plays a key role in natural language understanding. However, existing NLP approaches, not only traditional word2vec approach or contextualized transformer-based language models, fail to learn numeracy. As the result, the performance of these models is limited when they are applied to number-intensive applications in clinical and financial domains. In this work, we propose a simple number embedding approach based on knowledge graph. We construct a knowledge graph consisting of number entities and magnitude relations. Knowledge graph embedding method is then applied to obtain number vectors. Our approach is easy to implement, and experiment results on various numeracy-related NLP tasks demonstrate the effectiveness and efficiency of our method.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.221</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph
%A Duan, Hanyu
%A Yang, Yi
%A Tam, Kar Yan
%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 duan-etal-2021-learning-numeracy
%X Numeracy plays a key role in natural language understanding. However, existing NLP approaches, not only traditional word2vec approach or contextualized transformer-based language models, fail to learn numeracy. As the result, the performance of these models is limited when they are applied to number-intensive applications in clinical and financial domains. In this work, we propose a simple number embedding approach based on knowledge graph. We construct a knowledge graph consisting of number entities and magnitude relations. Knowledge graph embedding method is then applied to obtain number vectors. Our approach is easy to implement, and experiment results on various numeracy-related NLP tasks demonstrate the effectiveness and efficiency of our method.
%R 10.18653/v1/2021.findings-emnlp.221
%U https://aclanthology.org/2021.findings-emnlp.221
%U https://doi.org/10.18653/v1/2021.findings-emnlp.221
%P 2597-2602

Markdown (Informal)

[Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph](https://aclanthology.org/2021.findings-emnlp.221) (Duan et al., Findings 2021)

• Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph (Duan et al., Findings 2021)

ACL

• Hanyu Duan, Yi Yang, and Kar Yan Tam. 2021. Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 2597–2602, Punta Cana, Dominican Republic. Association for Computational Linguistics.