Supervised Disambiguation Bayesian classification We do not usually know p(sko), but we can use Baye Rule to compute it: P(k|)=P(|s/P()×P() P(k is the prior probability of Se,1.e, the probability of instance s, without any contextual information When updating the prior with evidence from context (i.e P(CSe/P(), we obtain the posterior probability P(e S If all we want to do is select the correct class, we can ignore P(. Also use logs to simplify computation Assign word w sense s'=argmax kp(sd argmaxeP(c5kX P(k= argmax klog P(c sk+ log P 20212/5 Natural Language Processing--Word Sense Disambiguation 82021/2/5 Natural Language Processing -- Word Sense Disambiguation 8 Supervised Disambiguation: Bayesian Classification ◼ We do not usually know P(sk|c), but we can use Bayes’ Rule to compute it: ◼ P(sk|c) = (P(c|sk )/P(c)) × P(sk ) ◼ P(sk ) is the prior probability of sk , i.e., the probability of instance sk without any contextual information. ◼ When updating the prior with evidence from context (i.e., P(c|sk )/P(c)), we obtain the posterior probability P(sk|c). ◼ If all we want to do is select the correct class, we can ignore P(c). Also use logs to simplify computation. ◼ Assign word w sense s ’ = argmaxskP(sk|c) =argmaxskP(c|sk ) × P(sk ) = argmaxsk[log P(c| sk ) + log P(sk )]