Next Article in Journal
Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home
Previous Article in Journal
Correntropy-Based Pulse Rate Variability Analysis in Children with Sleep Disordered Breathing
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
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Entropy 2017, 19(6), 283;
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
Show Figures

Figure 1

MDPI and ACS Style

Zeng, D.; Sun, C.; Lin, L.; Liu, B. LSTM-CRF for Drug-Named Entity Recognition. Entropy 2017, 19, 283.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop