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Lexical Ambiguity Resolution / Sense Disambiguation. Supervised methods Non-supervised methods Class-based models Seed models Vector models EM Iteration Unsupervised clustering Sense induction Anaphosa Resolution. Problem with supervised methods.
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Lexical Ambiguity Resolution / Sense Disambiguation • Supervised methods • Non-supervised methods • Class-based models • Seed models • Vector models • EM Iteration • Unsupervised clustering • Sense induction • Anaphosa Resolution -- CS466 Lecture XVIII --
Problem with supervised methods • Tagged training data is expensive (time, resources) • Solution: • Class discriminators can serve as • effective wordsense discriminators • And are much less costly to train if we can tolerate some noise in the models -- CS466 Lecture XVIII --
Pseudo-Class Discriminators What if class lists (like Rogets) are not available? Create small classes optimized for the target ambiguity Class (Crane 1) = heron, stork, eagle, condor, … Class (Crane 2) = derrick, forklift, bulldozers, … Class (Tank 1) = Jeep, Vehicle, Humvee, Bradley, Abrams, … Class (Tank 2) = Vessel, container, flask, pool Include synonyms, hype-nyms, hyponyms, topically related Smaller and potentially more specific but less robust (parent in tree) (child in tree) -- CS466 Lecture XVIII --
