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
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- 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
- 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-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/ingest-2024-clasp/D19-1648.pdf