Paper: Learning Word Sense Distributions, Detecting Unattested Senses and Identifying Novel Senses Using Topic Models

ACL ID P14-1025
Title Learning Word Sense Distributions, Detecting Unattested Senses and Identifying Novel Senses Using Topic Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Unsupervised word sense disambiguation (WSD) methods are an attractive approach to all-words WSD due to their non-reliance on expensive annotated data. Unsuper- vised estimates of sense frequency have been shown to be very useful for WSD due to the skewed nature of word sense distri- butions. This paper presents a fully unsu- pervised topic modelling-based approach to sense frequency estimation, which is highly portable to different corpora and sense inventories, in being applicable to any part of speech, and not requiring a hi- erarchical sense inventory, parsing or par- allel text. We demonstrate the effective- ness of the method over the tasks of pre- dominant sense learning and sense distri- bution acquisition, and also the novel tasks of detecting senses which aren?t attested in the c...