Collective Disambiguation
nObjects:
words in text
nAttributes:
sense, gender, number, pos, …
nLinks:
nGrammatical
relations (subject-object, modifier,…)
nClose
semantic relations (is-a, cause-of, …)
nSame
word in different sentences (one-sense-per-discourse)
nCompatibility
parameters:
nLearned
from tagged data
nBased
on prior knowledge (e.g., WordNet, FrameNet)
Her advisor gave her feedback about the
draft.
financial
academic
physical
figurative
electrical
criticism
wind
paper
Can we infer grammatical structure
and disambiguate word senses
simultaneously rather than sequentially?
Can we integrate inter-word relationships directly into our probabilistic model?