Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts

BACKGROUND: We aim to solve the problem of determining word senses for ambiguous biomedical terms with minimal human effort. METHODS: We build a fully automated system for Word Sense Disambiguation by designing a system that does not require manually-constructed external resources or manually-labele...

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Detalles Bibliográficos
Autores principales: Duan, Weisi, Song, Min, Yates, Alexander
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2665052/
https://www.ncbi.nlm.nih.gov/pubmed/19344480
http://dx.doi.org/10.1186/1471-2105-10-S3-S4
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author Duan, Weisi
Song, Min
Yates, Alexander
author_facet Duan, Weisi
Song, Min
Yates, Alexander
author_sort Duan, Weisi
collection PubMed
description BACKGROUND: We aim to solve the problem of determining word senses for ambiguous biomedical terms with minimal human effort. METHODS: We build a fully automated system for Word Sense Disambiguation by designing a system that does not require manually-constructed external resources or manually-labeled training examples except for a single ambiguous word. The system uses a novel and efficient graph-based algorithm to cluster words into groups that have the same meaning. Our algorithm follows the principle of finding a maximum margin between clusters, determining a split of the data that maximizes the minimum distance between pairs of data points belonging to two different clusters. RESULTS: On a test set of 21 ambiguous keywords from PubMed abstracts, our system has an average accuracy of 78%, outperforming a state-of-the-art unsupervised system by 2% and a baseline technique by 23%. On a standard data set from the National Library of Medicine, our system outperforms the baseline by 6% and comes within 5% of the accuracy of a supervised system. CONCLUSION: Our system is a novel, state-of-the-art technique for efficiently finding word sense clusters, and does not require training data or human effort for each new word to be disambiguated.
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spelling pubmed-26650522009-04-06 Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts Duan, Weisi Song, Min Yates, Alexander BMC Bioinformatics Proceedings BACKGROUND: We aim to solve the problem of determining word senses for ambiguous biomedical terms with minimal human effort. METHODS: We build a fully automated system for Word Sense Disambiguation by designing a system that does not require manually-constructed external resources or manually-labeled training examples except for a single ambiguous word. The system uses a novel and efficient graph-based algorithm to cluster words into groups that have the same meaning. Our algorithm follows the principle of finding a maximum margin between clusters, determining a split of the data that maximizes the minimum distance between pairs of data points belonging to two different clusters. RESULTS: On a test set of 21 ambiguous keywords from PubMed abstracts, our system has an average accuracy of 78%, outperforming a state-of-the-art unsupervised system by 2% and a baseline technique by 23%. On a standard data set from the National Library of Medicine, our system outperforms the baseline by 6% and comes within 5% of the accuracy of a supervised system. CONCLUSION: Our system is a novel, state-of-the-art technique for efficiently finding word sense clusters, and does not require training data or human effort for each new word to be disambiguated. BioMed Central 2009-03-19 /pmc/articles/PMC2665052/ /pubmed/19344480 http://dx.doi.org/10.1186/1471-2105-10-S3-S4 Text en Copyright © 2009 Duan et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Duan, Weisi
Song, Min
Yates, Alexander
Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts
title Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts
title_full Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts
title_fullStr Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts
title_full_unstemmed Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts
title_short Fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts
title_sort fast max-margin clustering for unsupervised word sense disambiguation in biomedical texts
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2665052/
https://www.ncbi.nlm.nih.gov/pubmed/19344480
http://dx.doi.org/10.1186/1471-2105-10-S3-S4
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