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This article is part of the supplement: Second International Workshop on Data and Text Mining in Bioinformatics (DTMBio) 2008

Open Access Proceedings

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

Weisi Duan1, Min Song2 and Alexander Yates1*

Author Affiliations

1 Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA

2 Information Systems Department, New Jersey Institute of Technology, Newark, NJ 07102, USA

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BMC Bioinformatics 2009, 10(Suppl 3):S4  doi:10.1186/1471-2105-10-S3-S4

Published: 19 March 2009



We aim to solve the problem of determining word senses for ambiguous biomedical terms with minimal human effort.


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.


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.


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.