Paper: Learning to Predict Distributions of Words Across Domains

ACL ID P14-1058
Title Learning to Predict Distributions of Words Across Domains
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

Although the distributional hypothesis has been applied successfully in many natural language processing tasks, systems using distributional information have been lim- ited to a single domain because the dis- tribution of a word can vary between do- mains as the word?s predominant mean- ing changes. However, if it were pos- sible to predict how the distribution of a word changes from one domain to an- other, the predictions could be used to adapt a system trained in one domain to work in another. We propose an unsuper- vised method to predict the distribution of a word in one domain, given its distribu- tion in another domain. We evaluate our method on two tasks: cross-domain part- of-speech tagging and cross-domain sen- timent classification. In both tasks, our method significantly outper...