Abstract: We study distributional similarity measures for the purpose of improving probability estimation for unseen cooccurrences. Our contributions are three-fold: an empirical comparison of a broad range of measures; a classification of similarity functions based on the information that they incorporate; and the introduction of a novel function that is superior at evaluating potential proxy distributions.

Paper formats: ps, pdf, other

Data: http://www.cs.cornell.edu/home/llee/data/sim.html

BibTeX entry:

@InProceedings{Lee:99a,
  author = 	 {Lillian Lee},
  title = 	 {Measures of Distributional Similarity},
  booktitle = 	 "37th Annual Meeting of the Association for Computational Linguistics",
  pages={25--32},
  year =	 1999,
}