Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries

@article{Liu2015EffectsOS,
  title={Effects of Semantic Features on Machine Learning-Based Drug Name Recognition Systems: Word Embeddings vs. Manually Constructed Dictionaries},
  author={Shengyu Liu and Buzhou Tang and Qingcai Chen and Xiaolong Wang},
  journal={Information},
  year={2015},
  volume={6},
  pages={848-865}
}
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 widely used to… CONTINUE READING
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UTurku: Drug named entity detection and drug-drug interaction extraction using SVM classification and domain knowledge

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