Multi-Task Learning for Chemical Named Entity Recognition with Chemical Compound Paraphrasing (D19-1)
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| Challenge: | Named entity recognition (NER) is one of the important basic technologies for Natural Language Processing (NLP) . |
| Approach: | They propose to use long short-term memory (LSTM) of NER model to capture chemical com- pound paraphrases by sharing parameters of LSTM and character embeddings be- tween the two models. |
| Outcome: | The proposed method improves chemi- cal NER and achieves state-of-the-art performance on the BioCreative IV’s CHEMDNER task. |
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