Tor, TorMd: Distributional Profiles of Concepts for Unsupervised Word Sense Disambiguation

Words in the context of a target word have long been used as features by supervised word-sense classifiers. Mohammad and Hirst (2006a) 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.