This paper presents a view of Spoken Language Dialog Systems in which a Domain Model is the unifying feature, providing a common representation of knowledge about the domain of application that is shared by many components of the system. The information in the Domain Model is used in different ways by the grammar and parser, the speech recognizer, the dialog manager, and the back end interface....

In this paper we describe how we have adapted the syntactic analyzer of written Estonian to the spoken language. The Constraint Grammar shallow syntactic parser (Müürisep et al. 2003) was used for the automatic syntactic analysis of the corpus of Estonian spoken language (Hennoste et al. 2000). To adapt the parser, the clause boundary detection rules as well as some syntactic constraints had to...

This paper describes an incremental parser based on an adjoining operation. By using the operation, we can avoid the problem of infinite local ambiguity. This paper further proposes a restricted version of the adjoining operation, which preserves lexical dependencies of partial parse trees. Our experimental results showed that the restriction enhances the accuracy of the incremental parsing. ke...

This paper describes the language output component of the Sundial spoken dialogue system. Following a dialogue planning decision to produce an utterance, an utterance planning component derives a semantic description drawing on a model of speakers' knowledge of the domain. This semantic description is linguistically real-ised by a generation component which draws on a bi-directional lexicon-gra...

We present an overview of the development environment for Regulus, an Open Source platform for construction of grammar-based speech-enabled systems, focussing on recent work whose goal has been to introduce uniformity between text and speech views of Regulus-based applications. We argue the advantages of being able to switch quickly between text and speech modalities in interactive and offline ...

Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken-language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component. In contrast, we describe...

Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken-language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component. In contrast, we describe...

Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken-language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component. In contrast, we describe...

This paper describes a grammar learning system which combines model-based and data-driven learning within a single framework. Results from learning grammars with the Spoken English Corpus (SEC) suggest that a combined model-based and data-driven learner can acquire a wide coverage grammar from only a small training corpus. In this paper, we present some results of our grammar learning system. W...

Previous approaches of analyzing spontaneously spoken language often have been based on encoding syntactic and semantic knowledge manually and symbolically. While there has been some progress using statistical or connectionist language models, many current spoken-language systems still use a relatively brittle, hand-coded symbolic grammar or symbolic semantic component. In contrast, we describe...