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

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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