1. Fall 2004 Lecture Notes #5 EECS 595 / LING 541 / SI 661 Natural Language Processing

2. Lexicalized and probabilistic parsing

3. Probabilistic CFG G (N, Σ, P, S) Non-terminals (N) Terminals (Σ) Productions (P) augmented with probabilities: A  β [p]

4. Disambiguation as a probability problem P(T,S) = P(T) P(S|T) = P(T) P(T l ) =.15 *.40 *.05 *.05 *.35 *.75 *.40 *.40 *.40 *.30 *.40 *.50 = 1.5 x 10 -6 P( Tr ) =.15 *.40 *.40 *.05 *.05 *.75 *.40 *.40 *.40 *.30 *.40 *.50 = 1.7 x 10 -6

5. Probabilistic parsing Probabilistic Earley algorithm –Top-down parser with a dynamic programming table Cocke-Younger-Kasami (CYK) algorithm –Bottom-up parser with a dynamic programming table Probabilities come from a Treebank.

6. Dependency grammars Lexical dependencies between head words Top-level predicate of a sentence is the root Useful for free word order languages Also simpler to parse

7. Dependencies John likes tabby cats NNPVBSJJNNS NP VP NP S

9. Readings for next time J&M Chapters 14, 15 Lecture notes #7, 8