@inproceedings{zhang-yang-2018-chinese,
title = "{C}hinese {NER} Using Lattice {LSTM}",
author = "Zhang, Yue and
Yang, Jie",
editor = "Gurevych, Iryna and
Miyao, Yusuke",
booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2018",
address = "Melbourne, Australia",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P18-1144",
doi = "10.18653/v1/P18-1144",
pages = "1554--1564",
abstract = "We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.",
}
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%0 Conference Proceedings
%T Chinese NER Using Lattice LSTM
%A Zhang, Yue
%A Yang, Jie
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F zhang-yang-2018-chinese
%X We investigate a lattice-structured LSTM model for Chinese NER, which encodes a sequence of input characters as well as all potential words that match a lexicon. Compared with character-based methods, our model explicitly leverages word and word sequence information. Compared with word-based methods, lattice LSTM does not suffer from segmentation errors. Gated recurrent cells allow our model to choose the most relevant characters and words from a sentence for better NER results. Experiments on various datasets show that lattice LSTM outperforms both word-based and character-based LSTM baselines, achieving the best results.
%R 10.18653/v1/P18-1144
%U https://aclanthology.org/P18-1144
%U https://doi.org/10.18653/v1/P18-1144
%P 1554-1564
Markdown (Informal)
[Chinese NER Using Lattice LSTM](https://aclanthology.org/P18-1144) (Zhang & Yang, ACL 2018)
ACL
- Yue Zhang and Jie Yang. 2018. Chinese NER Using Lattice LSTM. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1554–1564, Melbourne, Australia. Association for Computational Linguistics.