Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing

Taiki Watanabe, Akihiro Tamura, Takashi Ninomiya, Takuya Makino, Tomoya Iwakura


Abstract
We propose a method to improve named entity recognition (NER) for chemical compounds using multi-task learning by jointly training a chemical NER model and a chemical com- pound paraphrase model. Our method en- ables the long short-term memory (LSTM) of the NER model to capture chemical com- pound paraphrases by sharing the parameters of the LSTM and character embeddings be- tween the two models. The experimental re- sults on the BioCreative IV’s CHEMDNER task show that our method improves chemi- cal NER and achieves state-of-the-art perfor- mance.
Anthology ID:
D19-1648
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:
6244–6249
Language:
URL:
https://aclanthology.org/D19-1648
DOI:
10.18653/v1/D19-1648
Bibkey:
Cite (ACL):
Taiki Watanabe, Akihiro Tamura, Takashi Ninomiya, Takuya Makino, and Tomoya Iwakura. 2019. Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing. 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 6244–6249, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing (Watanabe et al., EMNLP 2019)
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PDF:
https://aclanthology.org/D19-1648.pdf