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        <title>Neural Chinese Address Parsing</title>
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    <name type="personal">
        <namePart type="given">Pengjun</namePart>
        <namePart type="family">Xie</namePart>
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    <name type="personal">
        <namePart type="given">Linlin</namePart>
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    <abstract>This paper introduces a new task – Chinese address parsing – the task of mapping Chinese addresses into semantically meaningful chunks. While it is possible to model this problem using a conventional sequence labelling approach, our observation is that there exist complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. We investigate neural structured prediction models with latent variables to capture such rich structural information within Chinese addresses. We create and publicly release a new dataset consisting of 15K Chinese addresses, and conduct extensive experiments on the dataset to investigate the model effectiveness and robustness. We release our code and data at http://statnlp.org/research/sp.</abstract>
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    <identifier type="doi">10.18653/v1/N19-1346</identifier>
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%0 Conference Proceedings
%T Neural Chinese Address Parsing
%A Li, Hao
%A Lu, Wei
%A Xie, Pengjun
%A Li, Linlin
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F li-etal-2019-neural-chinese
%X This paper introduces a new task – Chinese address parsing – the task of mapping Chinese addresses into semantically meaningful chunks. While it is possible to model this problem using a conventional sequence labelling approach, our observation is that there exist complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. We investigate neural structured prediction models with latent variables to capture such rich structural information within Chinese addresses. We create and publicly release a new dataset consisting of 15K Chinese addresses, and conduct extensive experiments on the dataset to investigate the model effectiveness and robustness. We release our code and data at http://statnlp.org/research/sp.
%R 10.18653/v1/N19-1346
%U https://aclanthology.org/N19-1346
%U https://doi.org/10.18653/v1/N19-1346
%P 3421-3431

Markdown (Informal)

[Neural Chinese Address Parsing](https://aclanthology.org/N19-1346) (Li et al., NAACL 2019)

• Neural Chinese Address Parsing (Li et al., NAACL 2019)

ACL

• Hao Li, Wei Lu, Pengjun Xie, and Linlin Li. 2019. Neural Chinese Address Parsing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3421–3431, Minneapolis, Minnesota. Association for Computational Linguistics.