Lexical Ambiguity Resolution / Sense Disambiguation

1
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

2
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

3
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)