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Open AccessArticle

LSTM-CRF for Drug-Named Entity Recognition

School of Computer Science and Technology, Harbin Institute of Technology, 92 West Dazhi Street, Harbin 150001, China
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Academic Editor: Raúl Alcaraz Martínez
Entropy 2017, 19(6), 283; https://doi.org/10.3390/e19060283
Received: 1 April 2017 / Revised: 9 June 2017 / Accepted: 9 June 2017 / Published: 17 June 2017
(This article belongs to the Section Information Theory, Probability and Statistics)
Drug-Named Entity Recognition (DNER) for biomedical literature is a fundamental facilitator of Information Extraction. For this reason, the DDIExtraction2011 (DDI2011) and DDIExtraction2013 (DDI2013) challenge introduced one task aiming at recognition of drug names. State-of-the-art DNER approaches heavily rely on hand-engineered features and domain-specific knowledge which are difficult to collect and define. Therefore, we offer an automatic exploring words and characters level features approach: a recurrent neural network using bidirectional long short-term memory (LSTM) with Conditional Random Fields decoding (LSTM-CRF). Two kinds of word representations are used in this work: word embedding, which is trained from a large amount of text, and character-based representation, which can capture orthographic feature of words. Experimental results on the DDI2011 and DDI2013 dataset show the effect of the proposed LSTM-CRF method. Our method outperforms the best system in the DDI2013 challenge. View Full-Text
Keywords: drug name entity recognition; information extraction; long short-term memory; conditional random field drug name entity recognition; information extraction; long short-term memory; conditional random field
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Zeng, D.; Sun, C.; Lin, L.; Liu, B. LSTM-CRF for Drug-Named Entity Recognition. Entropy 2017, 19, 283.

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