Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems

Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems

Open AccessThis article is freely available re-usable Information 2015, 6(4), 848-865; doi:10.3390/info6040848 (registering DOI) Article Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen 518055, China * Author to whom correspondence should be addressed. Received: 17 October 2015 / Revised: 4 December 2015 / Accepted: 4 December 2015 / Published: 11 December 2015 No Semantic features are very important for machine learning-based drug name recognition (DNR) systems. The semantic features used in most DNR systems are based on drug dictionaries manually constructed by experts. Building large-scale drug dictionaries is a time-consuming task and adding new drugs to existing drug dictionaries immediately after they are developed is also a challenge. In recent years, word embeddings that contain rich latent semantic information of words have been…


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