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Improving nonparameteric Bayesian inference: Experiments on unsupervised word segmentation with adaptor grammars

Year: 2009

Type: conference-paper

Abstract: One of the reasons nonparametric Bayesian inference is attracting attention in computational linguistics is because it provides a principled way of learning the units of generalization together with their probabilities. Adaptor grammars are a framework for defining a variety of hierarchical nonparametric Bayesian models. This paper investigates some of the choices that arise in formulating adaptor grammars and associated inference procedures, and shows that they can have a dramatic impact on performance in an unsupervised word segmentation task. With appropriate adaptor grammars and inference procedures we achieve an 87% word token f-score on the standard Brent version of the Bernstein- Ratner corpus, which is an error reduction of over 35% over the best previously reported results for this corpus.

Source: Edinburgh Research Explorer (University of Edinburgh)

Authors: Johnson, Mark, Goldwater, Sharon; id_orcid 0000-0002-7298-0947

Institution: Brown University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: National Science Foundation

Awards: Synergies in Computational Models of Morpho-phonological Learning, Collaborative research: Bayesian methods for learning and analyzing natural languages

OpenAlex ID: W7099414206

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