This paper describes a hybrid proposal to combine n-grams and Stochastic Context-Free Grammars (SCFGs) for language modeling. A classical n-gram model is used to capture the local relations between words, while a stochastic grammatical model is considered to represent the long-term relations between syntactical structures. In order to dene this grammatical model, which will be used on large-vocabulary complex tasks, a category-based SCFG and a probabilistic model of word distribution in the categories have been proposed. Methods for learning these stochastic models for complex tasks are described, and algorithms for computing the word transition probabilities are also presented. Finally, experiments using the Penn Treebank corpus improved by 30% the test set perplexity with regard to the classical n-gram models.

Some of the most widely-known methods to obtain Stochastic Context-Free Grammars (SCFGs) are based on estimation algorithms. ALl of these algorithms maximize a cetrain criterion function from a training sample...

The RNA sentences present structured regions caused by palindrome pairs and non-structured regions where any global relations can be found. In this paper, we present a combination of stochastic context-free grammars (SCFG) and bigram models. The SCFGs are used to represent the long-term relations of the structured part, while the bigram models are used to capture the local relations of the non-structured part of RNA sentences. A stochastic version of the Sakakibara algorithm is used to learn the SCFGs. Finally, experiments to evaluate the behavior of this proposal were carried out.

Lexical decoding is the obtaining of the most probable sequence of categories associated to a sequence of words. This paper describes two lexical decoding combined models which are based on a stochastic category-based model and a probabilistic model of word distribution into linguistic categories. In the rst combined model, the stochastic category-based model is a Stochastic ContextFree Grammar, and in the second combined model, the stochastic categorybased model is a n-gram model. The estimation processes of the models are described in detail. Finally, experiments on the Wall Street Journal corpus are reported.