Sixth International Conference on Spoken Language Processing
October 16-20, 2000
Combining Semantic and Syntactic Structure for Language Modeling
Informatics Research Institute, University of Leeds, Leeds, UK and
Institute for Logic, Language and Computation, University of Amsterdam,
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.
Bod, Rens (2000):
"Combining semantic and syntactic structure for language modeling",
In ICSLP-2000, vol.3, 106-109.