A comparison of PCFG models
Jose L. Verdú-Mas, Jorge Calera-Rubio, Rafael C. Carrasco · 2000
In this paper, we compare three different approaches to build a probabilistic context-free grammar for natural language parsing from a tree bank corpus: 1) a model that simply extracts 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 generalized k-gram scheme with k = 3. The last one allows for a faster parsing and decreases the perplexity of test samples.