Learning to Predict Distributions of Words Across Domains

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.

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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