TOR, TORMD: Distributional Profiles of Concepts
for Unsupervised Word Sense Disambiguation

Saif Mohammad, Graeme Hirst, and Philip Resnik

In Proceedings  of the Fourth International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (SemEval-07), June 2007, Prague, Czech Republic.
ABSTRACT: Words in the context of a target word have long been used as features by supervised word-sense classifiers. Mohammad and Hirst (2006) proposed a way to determine the strength of association between a sense or concept and co-occurring words---the distributional profile of a concept (DPC)---without the use of manually annotated data. We implemented an unsupervised naive Bayes word sense classifier using these DPCs that was best or within one percentage point of the best unsupervised systems in the Multilingual Chinese--English Lexical Sample Task (task #5) and the English Lexical Sample Task (task #17). We also created a simple PMI-based classifier to attempt the English Lexical Substitution Task (task #10); however, its performance was poor.

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