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Learning to predict distributions of words across domains

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conference contribution
posted on 2023-06-08, 20:35 authored by Danushka Bollegala, David WeirDavid Weir, John Carroll
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 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.

History

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 Linguistics

Page range

613-623

Event name

52nd Annual Meeting of the Association for Computational Linguistics

Event location

Baltimore, Maryland, USA

Event type

conference

Event date

23-25 June 2014

ISBN

9781937284725

Department affiliated with

  • Informatics Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2015-04-23

First Open Access (FOA) Date

2015-04-23

First Compliant Deposit (FCD) Date

2015-04-23

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