@inproceedings{watanabe-etal-2019-multi,
title = "Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing",
author = "Watanabe, Taiki and
Tamura, Akihiro and
Ninomiya, Takashi and
Makino, Takuya and
Iwakura, Tomoya",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1648/",
doi = "10.18653/v1/D19-1648",
pages = "6244--6249",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing
%A Watanabe, Taiki
%A Tamura, Akihiro
%A Ninomiya, Takashi
%A Makino, Takuya
%A Iwakura, Tomoya
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F watanabe-etal-2019-multi
%X 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.
%R 10.18653/v1/D19-1648
%U https://aclanthology.org/D19-1648/
%U https://doi.org/10.18653/v1/D19-1648
%P 6244-6249
Markdown (Informal)
[Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing](https://aclanthology.org/D19-1648/) (Watanabe et al., EMNLP-IJCNLP 2019)
ACL