Neural Chinese Address Parsing

Hao Li, Wei Lu, Pengjun Xie, Linlin Li


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.
Anthology ID:
N19-1346
Volume:
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)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3421–3431
Language:
URL:
https://www.aclweb.org/anthology/N19-1346
DOI:
10.18653/v1/N19-1346
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1346.pdf
Supplementary:
 N19-1346.Supplementary.pdf