'Rule probabilities' presentation slideshows

Probabilistic and Lexicalized Parsing

Probabilistic and Lexicalized Parsing. CS 4705. Probabilistic CFGs: PCFGs. Weighted CFGs Attach weights to rules of CFG Compute weights of derivations Use weights to choose preferred parses Utility: Pruning and ordering the search space, disambiguate, Language Model for ASR

490 views • 37 slides

Probabilistic and Lexicalized Parsing

Probabilistic and Lexicalized Parsing. CS 4705. Probabilistic CFGs: PCFGs. Weighted CFGs Attach weights to rules of CFG Compute weights of derivations Use weights to choose preferred parses Utility: Pruning and ordering the search space, disambiguate, Language Model for ASR

514 views • 37 slides

Probabilistic and Lexicalized Parsing

Probabilistic and Lexicalized Parsing. CS 4705. Probabilistic CFGs: PCFGs. Weighted CFGs Attach weights to rules of CFG Compute weights of derivations Use weights to choose preferred parses Utility: Pruning and ordering the search space, disambiguate, Language Model for ASR

486 views • 37 slides

Probabilistic and Lexicalized Parsing

Probabilistic and Lexicalized Parsing. Probabilistic CFGs. Weighted CFGs Attach weights to rules of CFG Compute weights of derivations Use weights to pick, preferred parses Utility: Pruning and ordering the search space, disambiguate, Language Model for ASR.

464 views • 30 slides

Statistical Parsing Chapter 14

421 views • 24 slides

From Grammar to N-grams

From Grammar to N-grams. Estimating N-grams From a Context-Free Grammar and Sparse Data. Thomas K Harris May 16, 2002. Motivation. Recognizers typically use n-grams. Systems are typically defined by CFGs. Data collection is difficult.

291 views • 17 slides

CS626-449: Speech, NLP and the Web/Topics in AI

CS626-449: Speech, NLP and the Web/Topics in AI. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture-15: Probabilistic parsing; PCFG (contd.). Example of Sentence labeling: Parsing. [ S1 [ S [ S [ VP [ VB Come][ NP [ NNP July]]]] [ , ,] [ CC and]

195 views • 10 slides