Bollegala, Danushka ORCID: 0000-0003-4476-7003, Weir, David and Carroll, John
(2014)
Learning to Predict Distributions of Words Across Domains.
In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2014-6 - 2014-6.
Text
ACL_2014.pdf - Published version Download (262kB) |
Abstract
Although the distributional hypothesis has been applied successfully in many natural language processing tasks, systems using distributional information have been limited to a single domain because the distribution of a word can vary between domains as the word's predominant meaning changes. However, if it were possible to predict how the distribution of a word changes from one domain to another, the predictions could be used to adapt a system trained in one domain to work in another. We propose an unsupervised method to predict the distribution of a word in one domain, given its distribution in another domain. We evaluate our method on two tasks: cross-domain partof- speech tagging and cross-domain sentiment classification. In both tasks, our method significantly outperforms competitive baselines and returns results that are statistically comparable to current stateof- the-art methods, while requiring no task-specific customisations. © 2014 Association for Computational Linguistics.
Item Type: | Conference or Workshop Item (Paper) |
---|---|
Subjects: | ?? QA75 ?? |
Depositing User: | Symplectic Admin |
Date Deposited: | 10 Feb 2015 09:09 |
Last Modified: | 15 Dec 2022 22:53 |
DOI: | 10.3115/v1/p14-1058 |
Related URLs: | |
URI: | https://livrepository.liverpool.ac.uk/id/eprint/2006351 |