Paper: Unsupervised Domain Tuning to Improve Word Sense Disambiguation

ACL ID N13-1079
Title Unsupervised Domain Tuning to Improve Word Sense Disambiguation
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013
Authors

The topic of a document can prove to be use- ful information for Word Sense Disambigua- tion (WSD) since certain meanings tend to be associated with particular topics. This paper presents an LDA-based approach for WSD, which is trained using any available WSD sys- tem to establish a sense per (Latent Dirich- let allocation based) topic. The technique is tested using three unsupervised and one su- pervised WSD algorithms within the SPORT and FINANCE domains giving a performance increase each time, suggesting that the tech- nique may be useful to improve the perfor- mance of any available WSD system.