Paper: Measuring Topic Homogeneity and its Application to Dictionary-Based Word Sense Disambiguation

ACL ID C08-1035
Title Measuring Topic Homogeneity and its Application to Dictionary-Based Word Sense Disambiguation
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

The use of topical features is abundant in Natural Language Processing (NLP), a major example being in dictionary-based Word Sense Disambiguation (WSD). Yet previous research does not attempt to measure the level of topic cohesion in documents, despite assertions of its ef- fects. This paper introduces a quantitative measure of Topic Homogeneity using a range of NLP resources and not requiring prior knowledge of correct senses. Eval- uation is performed firstly by using the WordNet::Domains package to create word-sets with varying levels of homo- geneity and comparing our results with those expected. Additionally, to evaluate each measure’s potential value, the ho- mogeneity results are correlated against those of 3 co-occurrence/dictionary- based WSD techniques, tested on...