An information extraction approach for spoken language understanding

Abstract

This paper presents an Information Extraction (IE) approach for spoken language understanding. The goal in IE is to find proper values for pre-defined slots of given templates. IE for spoken language understanding proposes a concept spotting approach for spoken language because IE approach is interested in only pre-defined concept slots. In spite of this partial understanding, we can acquire necessary information for an application from the values of pre-defined slots because the slots are properly designed for speech understanding in a specific domain. Spoken language has so many recognition errors especially in a poor environment so it is more difficult to understand than textual language. Considering this fact, we attempt to understand the languages by concentrating on the specified information. In experiments on the car navigation domain, F-measure for concept spotting for textual input (WER 0%) and spoken input (WER 39%) are 96.33% and 78.30% respectively.