Chemical Name Extraction Based on Automatic Training Data Generation and Rich Feature Set

@article{Yan2013ChemicalNE,
  title={Chemical Name Extraction Based on Automatic Training Data Generation and Rich Feature Set},
  author={Su Yan and W. Scott Spangler and Ying Chen},
  journal={IEEE/ACM Transactions on Computational Biology and Bioinformatics},
  year={2013},
  volume={10},
  pages={1218-1233}
}
The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating… CONTINUE READING
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