Semantic role labeling with discriminative feature selection for spoken language understanding

Chao Hong Liu, Chung Hsien Wu

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)

Abstract

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 information for SDS encounters two difficulties: 1) the stateof-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.

Original languageEnglish
Pages (from-to)1043-1046
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2009 Nov 26
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: 2009 Sep 62009 Sep 10

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

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