Paper: Exploiting Non-Local Features For Spoken Language Understanding

ACL ID P06-2054
Title Exploiting Non-Local Features For Spoken Language Understanding
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

In this paper, we exploit non-local fea- tures as an estimate of long-distance de- pendencies to improve performance on the statistical spoken language understanding (SLU) problem. The statistical natural language parsers trained on text perform unreliably to encode non-local informa- tion on spoken language. An alternative method we propose is to use trigger pairs that are automatically extracted by a fea- ture induction algorithm. We describe a light version of the inducer in which a sim- ple modification is efficient and success- ful. We evaluate our method on an SLU task and show an error reduction of up to 27% over the base local model.