LSTM-CRF for Drug-Named Entity Recognition
AbstractDrug-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
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Zeng, D.; Sun, C.; Lin, L.; Liu, B. LSTM-CRF for Drug-Named Entity Recognition. Entropy 2017, 19, 283.
Zeng D, Sun C, Lin L, Liu B. LSTM-CRF for Drug-Named Entity Recognition. Entropy. 2017; 19(6):283.Chicago/Turabian Style
Zeng, Donghuo; Sun, Chengjie; Lin, Lei; Liu, Bingquan. 2017. "LSTM-CRF for Drug-Named Entity Recognition." Entropy 19, no. 6: 283.
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