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The aim is to develop an unsupervised algorithm that effectively disambiguates word senses without the need for extensive manual annotation.

January 1, 1995Open Access

Unsupervised word sense disambiguation rivaling supervised methods

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The aim is to develop an unsupervised algorithm that effectively disambiguates word senses without the need for extensive manual annotation.
Developed an unsupervised learning algorithm based on two constraints: one sense per discourse and one sense per collocation.
Utilized an iterative bootstrapping procedure to enhance word sense identification.
Tested the algorithm on unannotated English text.
Achieved an accuracy exceeding 96% in sense disambiguation.
Performance rivals supervised methods that require hand annotations.

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Randomized trial reveals high accuracy of unsupervised disambiguation in unannotated text, suggesting potential cost savings.

David Yarowsky (1995) studied this question.

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