Multi-grained Named Entity Recognition

Congying Xia, Chenwei Zhang, Tao Yang, Yaliang Li, Nan Du, Xian Wu, Wei Fan, Fenglong Ma, Philip Yu


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.
Anthology ID:
P19-1138
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1430–1440
Language:
URL:
https://aclanthology.org/P19-1138
DOI:
10.18653/v1/P19-1138
Bibkey:
Cite (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.
Cite (Informal):
Multi-grained Named Entity Recognition (Xia et al., ACL 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/P19-1138.pdf
Software:
 P19-1138.Software.zip
Data
ACE 2004ACE 2005CoNLL-2003