Aprendizaje de gramáticas probabilísticas a partir de árboles sintácticos
ISSN: 1135-5948
Year of publication: 2003
Issue: 31
Pages: 175-182
Type: Article
More publications in: Procesamiento del lenguaje natural
Abstract
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 for trees with k = 3. The last model allows for faster parsing, decreases considerably the perplexity of test samples and may be seen as a generalization of the classic n-gram models to the case of trees.