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-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.


CONCLUSIONS
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.
Duan
Weisi
W
Department of Computer and Information Sciences, Temple University, Philadelphia, PA 19122, USA. weisi.duan@temple.edu
Song
Min
M
Yates
Alexander
A