@inproceedings{ding-etal-2019-neural,
title = "A Neural Multi-digraph Model for {C}hinese {NER} with Gazetteers",
author = "Ding, Ruixue and
Xie, Pengjun and
Zhang, Xiaoyan and
Lu, Wei and
Li, Linlin and
Si, Luo",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1141",
doi = "10.18653/v1/P19-1141",
pages = "1462--1467",
abstract = "Gazetteers were shown to be useful resources for named entity recognition (NER). Many existing approaches to incorporating gazetteers into machine learning based NER systems rely on manually defined selection strategies or handcrafted templates, which may not always lead to optimal effectiveness, especially when multiple gazetteers are involved. This is especially the case for the task of Chinese NER, where the words are not naturally tokenized, leading to additional ambiguities. To automatically learn how to incorporate multiple gazetteers into an NER system, we propose a novel approach based on graph neural networks with a multi-digraph structure that captures the information that the gazetteers offer. Experiments on various datasets show that our model is effective in incorporating rich gazetteer information while resolving ambiguities, outperforming previous approaches.",
}
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<abstract>Gazetteers were shown to be useful resources for named entity recognition (NER). Many existing approaches to incorporating gazetteers into machine learning based NER systems rely on manually defined selection strategies or handcrafted templates, which may not always lead to optimal effectiveness, especially when multiple gazetteers are involved. This is especially the case for the task of Chinese NER, where the words are not naturally tokenized, leading to additional ambiguities. To automatically learn how to incorporate multiple gazetteers into an NER system, we propose a novel approach based on graph neural networks with a multi-digraph structure that captures the information that the gazetteers offer. Experiments on various datasets show that our model is effective in incorporating rich gazetteer information while resolving ambiguities, outperforming previous approaches.</abstract>
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%0 Conference Proceedings
%T A Neural Multi-digraph Model for Chinese NER with Gazetteers
%A Ding, Ruixue
%A Xie, Pengjun
%A Zhang, Xiaoyan
%A Lu, Wei
%A Li, Linlin
%A Si, Luo
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F ding-etal-2019-neural
%X Gazetteers were shown to be useful resources for named entity recognition (NER). Many existing approaches to incorporating gazetteers into machine learning based NER systems rely on manually defined selection strategies or handcrafted templates, which may not always lead to optimal effectiveness, especially when multiple gazetteers are involved. This is especially the case for the task of Chinese NER, where the words are not naturally tokenized, leading to additional ambiguities. To automatically learn how to incorporate multiple gazetteers into an NER system, we propose a novel approach based on graph neural networks with a multi-digraph structure that captures the information that the gazetteers offer. Experiments on various datasets show that our model is effective in incorporating rich gazetteer information while resolving ambiguities, outperforming previous approaches.
%R 10.18653/v1/P19-1141
%U https://aclanthology.org/P19-1141
%U https://doi.org/10.18653/v1/P19-1141
%P 1462-1467
Markdown (Informal)
[A Neural Multi-digraph Model for Chinese NER with Gazetteers](https://aclanthology.org/P19-1141) (Ding et al., ACL 2019)
ACL
- Ruixue Ding, Pengjun Xie, Xiaoyan Zhang, Wei Lu, Linlin Li, and Luo Si. 2019. A Neural Multi-digraph Model for Chinese NER with Gazetteers. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1462–1467, Florence, Italy. Association for Computational Linguistics.