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        <title>Automatic rule generation for time expression normalization</title>
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        <namePart type="given">Jianhao</namePart>
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    <name type="personal">
        <namePart type="given">Yuzhong</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>
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            <namePart type="given">Xuanjing</namePart>
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            <namePart type="given">Lucia</namePart>
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    <abstract>The understanding of time expressions includes two sub-tasks: recognition and normalization. In recent years, significant progress has been made in the recognition of time expressions while research on normalization has lagged behind. Existing SOTA normalization methods highly rely on rules or grammars designed by experts, which limits their performance on emerging corpora, such as social media texts. In this paper, we model time expression normalization as a sequence of operations to construct the normalized temporal value, and we present a novel method called ARTime, which can automatically generate normalization rules from training data without expert interventions. Specifically, ARTime automatically captures possible operation sequences from annotated data and generates normalization rules on time expressions with common surface forms. The experimental results show that ARTime can significantly surpass SOTA methods on the Tweets benchmark, and achieves competitive results with existing expert-engineered rule methods on the TempEval-3 benchmark.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.269</identifier>
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%0 Conference Proceedings
%T Automatic rule generation for time expression normalization
%A Ding, Wentao
%A Chen, Jianhao
%A Li, Jinmao
%A Qu, Yuzhong
%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 ding-etal-2021-automatic-rule
%X The understanding of time expressions includes two sub-tasks: recognition and normalization. In recent years, significant progress has been made in the recognition of time expressions while research on normalization has lagged behind. Existing SOTA normalization methods highly rely on rules or grammars designed by experts, which limits their performance on emerging corpora, such as social media texts. In this paper, we model time expression normalization as a sequence of operations to construct the normalized temporal value, and we present a novel method called ARTime, which can automatically generate normalization rules from training data without expert interventions. Specifically, ARTime automatically captures possible operation sequences from annotated data and generates normalization rules on time expressions with common surface forms. The experimental results show that ARTime can significantly surpass SOTA methods on the Tweets benchmark, and achieves competitive results with existing expert-engineered rule methods on the TempEval-3 benchmark.
%R 10.18653/v1/2021.findings-emnlp.269
%U https://aclanthology.org/2021.findings-emnlp.269
%U https://doi.org/10.18653/v1/2021.findings-emnlp.269
%P 3135-3144

Markdown (Informal)

[Automatic rule generation for time expression normalization](https://aclanthology.org/2021.findings-emnlp.269) (Ding et al., Findings 2021)

• Automatic rule generation for time expression normalization (Ding et al., Findings 2021)

ACL

• Wentao Ding, Jianhao Chen, Jinmao Li, and Yuzhong Qu. 2021. Automatic rule generation for time expression normalization. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3135–3144, Punta Cana, Dominican Republic. Association for Computational Linguistics.