Paper: A Comparison Of PCFG Models

ACL ID W00-0725
Title A Comparison Of PCFG Models
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2000
Authors

In this paper, we compare three different ap- proaches to build a probabilistic context-free grammar for natural language parsing from a tree bank corpus: 1) a model that simply ex- tracts the rules contained in the corpus and counts the number of occurrences of each rule 2) a model that also stores information about the parent node's category and, 3) a model that estimates the probabilities according to a gen- eralized k-gram scheme with k -- 3. The last one allows for a faster parsing and decreases the perplexity of test samples.