Applying natural language processing technique to language modeling is a key problem in speech recognition. This paper describes a maximum entropy-based approach to language modeling in which both words together with syntactic and semantic tags in the long history are used as a basis for complex linguistic questions. These questions are integrated with a standard trigram language model or a standard trigram language model combined with long history word triggers and the resulting language model is used to rescore the N-best hypotheses output of the ATRSPREC speech recognition system. The technique removed 24% of the correctable error of the recognition system.

Much recent research has demonstrated that the correlation between a language model's perplexity and its effect on the word error rate of a speech recognition system is not as strong as was once thought.This represents a major problem for those involved in developing language models.This paper describes the development of new measures of language model quality.These measures retain the ease of computation and task independence that are perplexity's strengths, yet are considerably better correlated with word error rate.This paper also shows that mixture-based language models are improved by applying interpolation weights which are optimised with respect to these new measures, rather than a maximum likelihood criterion.

We describe a generative probabilistic model of natural language, which we call HBG, that takes advantage of detailed linguistic information to resolve ambiguity. HBG incorporates lexical, syntactic, semantic, and structural information from the parse tree into the disambiguation process in a novel way. We use a corpus of bracketed sentences, called a Treebank, in combination with decision tree building to tease out the relevant aspects of a parse tree that will determine the correct parse of a sentence. This stands in contrast to the usual approach of further grammar tailoring via the usual linguistic introspection in the hope of generating the correct parse. In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error.

Americanae nace como un proyecto conjunto que surge dentro de la Red Europea de Información y Documentación sobre América Latina (REDIAL), y que ha afrontado la Biblioteca de la Agencia Española de Cooperación Internacional para el Desarrollo (AECID). Esta nueva biblioteca virtual hace más accesibles los libros digitales de tema americanista a los investigadores y usuarios interesados de cualquier parte del mundo.