Evaluating distributed word representations for capturing semantics of biomedical concepts

@inproceedings{Th2015EvaluatingDW,
  title={Evaluating distributed word representations for capturing semantics of biomedical concepts},
  author={Muneeb Th and Sunil Kumar Sahu and Ashish Anand},
  booktitle={BioNLP@IJCNLP},
  year={2015}
}
Recently there is a surge in interest in learning vector representations of words using huge corpus in unsupervised manner. Such word vector representations, also known as word embedding, have been shown to improve the performance of machine learning models in several NLP tasks. However efficiency of such representation has not been systematically evaluated in biomedical domain. In this work our aim is to compare the performance of two state-of-the-art word embedding methods, namely word2vec… CONTINUE READING
Highly Cited
This paper has 40 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 29 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 21 references

proving distributional similarity with lessons learned from word embeddings

  • Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean
  • Transactions of the Association for Computational…
  • 2015

Similar Papers

Loading similar papers…