Similarity Based Auxiliary Classifier for Named Entity Recognition

Shiyuan Xiao, Yuanxin Ouyang, Wenge Rong, Jianxin Yang, Zhang Xiong


Abstract
The segmentation problem is one of the fundamental challenges associated with name entity recognition (NER) tasks that aim to reduce the boundary error when detecting a sequence of entity words. A considerable number of advanced approaches have been proposed and most of them exhibit performance deterioration when entities become longer. Inspired by previous work in which a multi-task strategy is used to solve segmentation problems, we design a similarity based auxiliary classifier (SAC), which can distinguish entity words from non-entity words. Unlike conventional classifiers, SAC uses vectors to indicate tags. Therefore, SAC can calculate the similarities between words and tags, and then compute a weighted sum of the tag vectors, which can be considered a useful feature for NER tasks. Empirical results are used to verify the rationality of the SAC structure and demonstrate the SAC model’s potential in performance improvement against our baseline approaches.
Anthology ID:
D19-1105
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1140–1149
Language:
URL:
https://aclanthology.org/D19-1105
DOI:
10.18653/v1/D19-1105
Bibkey:
Cite (ACL):
Shiyuan Xiao, Yuanxin Ouyang, Wenge Rong, Jianxin Yang, and Zhang Xiong. 2019. Similarity Based Auxiliary Classifier for Named Entity Recognition. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1140–1149, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Similarity Based Auxiliary Classifier for Named Entity Recognition (Xiao et al., EMNLP-IJCNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1105.pdf
Data
WNUT 2017