Unsupervised Disambiguation

Idea: disambiguate word senses without having recourse to supporting tools such as dictionaries and thesauri and in the absence of labeled text. Simply cluster the contexts of an ambiguous word into a number of groups and discriminate between these groups without labeling them.

(Schutze, 1998): The probabilistic model is the same Bayesian model as the one used for supervised classification, but the P(vj | sk) are estimated using the EM algorithm.

Idea: disambiguate word senses without having recourse to supporting tools such as dictionaries and thesauri and in the absence of labeled text. Simply cluster the contexts of an ambiguous word into a number of groups and discriminate between these groups without labeling them.