Semantic filtering by inference on domain knowledge in spoken dialogue systems
<|reference_start|>Semantic filtering by inference on domain knowledge in spoken dialogue systems: General natural dialogue processing requires large amounts of domain knowledge as well as linguistic knowledge in order to ensure acceptable coverage and understanding. There are several ways of integrating lexical resources (e.g. dictionaries, thesauri) and knowledge bases or ontologies at different levels of dialogue processing. We concentrate in this paper on how to exploit domain knowledge for filtering interpretation hypotheses generated by a robust semantic parser. We use domain knowledge to semantically constrain the hypothesis space. Moreover, adding an inference mechanism allows us to complete the interpretation when information is not explicitly available. Further, we discuss briefly how this can be generalized towards a predictive natural interactive system.<|reference_end|>
@article{ballim2004semantic,
    title={Semantic filtering by inference on domain knowledge in spoken dialogue
  systems},
    author={Afzal Ballim, Vincenzo Pallotta},
    journal={Proceedings of the LREC 2000 Workshop "From spoken dialogue to
  full natural interactive dialogue. Theory, empirical analysis and
  evaluation", May 29th, 2000 Athen, Greece},
    year={2004},
    archivePrefix={arXiv},
    eprint={cs/0410060},
    primaryClass={cs.CL cs.AI cs.HC cs.IR}
}