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Recent contributions to statistical language modeling for speech recognition have shown that probabilistically parsing a partial word sequence aids the prediction of the next word, leading to "structured" language models that have the potential to outperform n-grams. Existing approaches to structured language modeling construct nodes in the partial parse tree after all of the underlying words have been predicted. This paper presents a different approach, based on probabilistic left-corner grammar (PLCG) parsing, that extends a partial parse both from the bottom up and from the top down, leading to a more focused and more accurate, though somewhat less robust, search of the parse space. At the core of our new structured language model is a fast context-sensitive and lexicalized PLCG parsing algorithm that uses dynamic programming. Preliminary perplexity and word-accuracy results appear to be competitive with previous ones, while speed is increased.

A structured language model based on context-sensitive probabilistic left-corner parsing