P14-1058.pdf (256.71 kB)
Learning to predict distributions of words across domains
conference contribution
posted on 2023-06-08, 20:35 authored by Danushka Bollegala, David WeirDavid Weir, John CarrollAlthough 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 part-of-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 state-of-the-art methods, while requiring no task-specific customisations.
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Publication status
- Published
File Version
- Published version
Journal
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)Publisher
The Association for Computational LinguisticsExternal DOI
Page range
613-623Event name
52nd Annual Meeting of the Association for Computational LinguisticsEvent location
Baltimore, Maryland, USAEvent type
conferenceEvent date
23-25 June 2014ISBN
9781937284725Department affiliated with
- Informatics Publications
Full text available
- Yes
Peer reviewed?
- Yes
Legacy Posted Date
2015-04-23First Open Access (FOA) Date
2015-04-23First Compliant Deposit (FCD) Date
2015-04-23Usage metrics
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