Ontology‐based speech act identification in a bilingual dialog system using partial pattern trees
Jui‐Feng Yeh,
Chung‐Hsien Wu and
Ming‐Jun Chen
Journal of the American Society for Information Science and Technology, 2008, vol. 59, issue 5, 684-694
Abstract:
This article presents a bilingual ontology‐based dialog system with multiple services. An ontology‐alignment algorithm is proposed to integrate ontologies of different languages for cross‐language applications. A domain‐specific ontology is further extracted from the bilingual ontology using an island‐driven algorithm and a domain corpus. This study extracts the semantic words/concepts using latent semantic analysis (LSA). Based on the extracted semantic words and the domain ontology, a partial pattern tree is constructed to model the speech act of a spoken utterance. The partial pattern tree is used to deal with the ill‐formed sentence problem in a spoken‐dialog system. Concept expansion based on domain ontology is also adopted to improve system performance. For performance evaluation, a medical dialog system with multiple services, including registration information, clinic information, and FAQ information, is implemented. Four performance measures were used separately for evaluation. The speech act identification rate was 86.2%. A task success rate of 77% was obtained. The contextual appropriateness of the system response was 78.5%. Finally, the rate for correct FAQ retrieval was 82%, an improvement of 15% over the keyword‐based vector‐space model. The results show the proposed ontology‐based speech‐act identification is effective for dialog management.
Date: 2008
References: Add references at CitEc
Citations:
Downloads: (external link)
https://doi.org/10.1002/asi.20700
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:59:y:2008:i:5:p:684-694
Ordering information: This journal article can be ordered from
https://doi.org/10.1002/(ISSN)1532-2890
Access Statistics for this article
More articles in Journal of the American Society for Information Science and Technology from Association for Information Science & Technology
Bibliographic data for series maintained by Wiley Content Delivery ().