Paper: Combining Statistical And Knowledge-Based Spoken Language Understanding In Conditional Models

ACL ID P06-2113
Title Combining Statistical And Knowledge-Based Spoken Language Understanding In Conditional Models
Venue Annual Meeting of the Association of Computational Linguistics
Session Poster Session
Year 2006
Authors

Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive -- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. On the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. A generative HMM/CFG composite model, which integrates easy-to- obtain domain knowledge into a data-driven statistical learning framework, has previously been introduced to reduce data requirement. The major contribution of this paper is the investigation of integr...