In the task of Spoken Language Understanding (SLU), Intent Classification techniques have been applied to different domains of Spoken Dialog Systems (SDS). Recently it was shown that intent classification performance can be improved with Semantic Role (SR) information. However, using SR information for SDS encounters two difficulties: 1) the state-of-the-art Automatic Speech Recognition (ASR) systems provide less than 80% recognition rate, 2) speech always exhibits ungrammatical expressions. This study presents an approach to Semantic Role Labeling (SRL) with discriminative feature selection to improve the performance of SDS. Bernoulli event features on word and part-of-speech sequences are introduced for better representation of the ASR recognized text. SRL and SLU experiments conducted using CoNLL-2005 SRL corpus and ATIS spoken corpus show that the proposed feature selection method with Bernoulli event features can improve intent classification by 3.4% and the performance of SRL.
Bibliographic reference. Liu, Chao-Hong / Wu, Chung-Hsien (2009): "Semantic role labeling with discriminative feature selection for spoken language understanding", In INTERSPEECH-2009, 1043-1046.