Combining semantic and syntactic structure for language modeling
<|reference_start|>Combining semantic and syntactic structure for language modeling: 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.<|reference_end|>
@article{bod2001combining,
    title={Combining semantic and syntactic structure for language modeling},
    author={Rens Bod},
    journal={Proceedings ICSLP'2000, Beijing, China},
    year={2001},
    archivePrefix={arXiv},
    eprint={cs/0110051},
    primaryClass={cs.CL}
}