Structured language models for speech recognition have been shown to remedy the weaknesses of n -gram models. All current structured language models, however, are limited in that they do not take into account dependencies between non-headwords. We show that non-headword dependencies contribute significantly to improved word error rate, and that a data-oriented parsing model trained on semantically and syntactically annotated data can exploit these dependencies. This paper contains the first published experiments with a data-oriented parsing model trained by means of a maximum likelihood reestimation procedure.
Cite as: Bod, R. (2000) Combining semantic and syntactic structure for language modeling. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 3, 106-109
@inproceedings{bod00_icslp,
author={Rens Bod},
title={{Combining semantic and syntactic structure for language modeling}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 3, 106-109}
}