Paper: Applying a Naive Bayes Similarity Measure to Word Sense Disambiguation

ACL ID P14-2087
Title Applying a Naive Bayes Similarity Measure to Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

We replace the overlap mechanism of the Lesk algorithm with a simple, general- purpose Naive Bayes model that mea- sures many-to-many association between two sets of random variables. Even with simple probability estimates such as max- imum likelihood, the model gains signifi- cant improvement over the Lesk algorithm on word sense disambiguation tasks. With additional lexical knowledge from Word- Net, performance is further improved to surpass the state-of-the-art results.