@inproceedings{xia-etal-2019-multi,
title = "Multi-grained Named Entity Recognition",
author = "Xia, Congying and
Zhang, Chenwei and
Yang, Tao and
Li, Yaliang and
Du, Nan and
Wu, Xian and
Fan, Wei and
Ma, Fenglong and
Yu, Philip",
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/P19-1138",
doi = "10.18653/v1/P19-1138",
pages = "1430--1440",
abstract = "This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4{\%} in terms of the F1 score among nested/non-overlapping NER tasks.",
}
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<abstract>This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.</abstract>
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%0 Conference Proceedings
%T Multi-grained Named Entity Recognition
%A Xia, Congying
%A Zhang, Chenwei
%A Yang, Tao
%A Li, Yaliang
%A Du, Nan
%A Wu, Xian
%A Fan, Wei
%A Ma, Fenglong
%A Yu, Philip
%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 xia-etal-2019-multi
%X This paper presents a novel framework, MGNER, for Multi-Grained Named Entity Recognition where multiple entities or entity mentions in a sentence could be non-overlapping or totally nested. Different from traditional approaches regarding NER as a sequential labeling task and annotate entities consecutively, MGNER detects and recognizes entities on multiple granularities: it is able to recognize named entities without explicitly assuming non-overlapping or totally nested structures. MGNER consists of a Detector that examines all possible word segments and a Classifier that categorizes entities. In addition, contextual information and a self-attention mechanism are utilized throughout the framework to improve the NER performance. Experimental results show that MGNER outperforms current state-of-the-art baselines up to 4.4% in terms of the F1 score among nested/non-overlapping NER tasks.
%R 10.18653/v1/P19-1138
%U https://aclanthology.org/P19-1138
%U https://doi.org/10.18653/v1/P19-1138
%P 1430-1440
Markdown (Informal)
[Multi-grained Named Entity Recognition](https://aclanthology.org/P19-1138) (Xia et al., ACL 2019)
ACL
- Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, and Philip Yu. 2019. Multi-grained Named Entity Recognition. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1430–1440, Florence, Italy. Association for Computational Linguistics.