Combining semantic and syntactic structure for language modeling

Rens Bod

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Combining semantic and syntactic structure for language modeling

Rens Bod

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Abstract

Structured language models for speech recognition have been shown to remedy the weaknesses of n-gram models. All current structured language models are, however, limited in that they do not take into account dependencies between non-headwords. We show that non-headword dependencies contribute to significantly improved word error rate, and that a data-oriented parsing model trained on semantically and syntactically annotated data can exploit these dependencies. This paper also contains the first DOP model trained by means of a maximum likelihood reestimation procedure, which solves some of the theoretical shortcomings of previous DOP models.

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Rens Bod. 2001-10-24. Combining semantic and syntactic structure for language modeling. https://arxiv.org/abs/cs/0110051

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