Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation

@article{JimenoYepes2017WordEA,
  title={Word embeddings and recurrent neural networks based on Long-Short Term Memory nodes in supervised biomedical word sense disambiguation},
  author={Antonio Jimeno-Yepes},
  journal={Journal of biomedical informatics},
  year={2017},
  volume={73},
  pages={
          137-147
        }
}
Word sense disambiguation helps identifying the proper sense of ambiguous words in text. With large terminologies such as the UMLS Metathesaurus ambiguities appear and highly effective disambiguation methods are required. Supervised learning algorithm methods are used as one of the approaches to perform disambiguation. Features extracted from the context of an ambiguous word are used to identify the proper sense of such a word. The type of features have an impact on machine learning methods… CONTINUE READING
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References

Publications referenced by this paper.
SHOWING 1-10 OF 42 REFERENCES