S. van der Burgt

Bio: S. van der Burgt is an academic researcher. The author has contributed to research in topics: Information system. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

Language analysis for dialogue management in a theatre information and booking system

Abstract: SCHISMA is a joint research project of KPN (Royal PTT Nederland) and the University of Twente. The project aims at providing a natural language dialogue system which interfaces a database containing information about theatre performances in a certain city or region. The interface should make it possible to ask about performances in general, to tune in to a specific performance and, if desired, make a reservation for this performance. Research until now has concentrated on various aspects of realising such a theatre information and booking system. Among these aspects are the building of a Wizard of Oz environment for the acquisition of a corpus of dialogues for this domain, analysis and tagging of the dialogue corpus, recognition of domain-specific concepts (actors, authors, plays, dates, etc.), syntactic analysis and dialogue modelling. The emphasis in this paper will be on the analysis of individual utterances at various levels. Most important for the project are short and medium term goals of delivering prototype systems that allow demonstration of the system and evaluation of the design choices. Due to these goals the project does not strive at incorporating advanced but isolated research results on discourse models and syntactic and semantic analysis. Rather we investigate how to identify user preferences and how to embed systems like these in a more comprehensive environment of information and transaction services.

Systems Engineering and Conversational Agents

Abstract: This chapter describes Conversational Agents (CAs) in the context of Systems Engineering. A CA is a computer program which interacts with a user through natural language dialogue and provides some form of service. CA technology has two points of interest to systems engineers: the use of systems engineering techniques in CA research and the application of CAs in project development. CAs offer the opportunity to automate more complex applications than are feasible with conventional web interfaces. Currently such applications require a human expert in the domain to mediate between the user and the application. The CA effectively replaces the human expert. This chapter reviews the current capabilities of various CA technologies, outlines a development methodology for systems engineering practitioners interested in developing real world applications and suggests a number of directions for systems engineers who wish to participate in CA research.

Feature selection for high dimensional data: An evolutionary filter approach.

Abstract: Problem statement: Feature selection is a task of crucial importance for the application of machine learning in various domains. In addition, the recent increase of data dimensionality poses a severe challenge to many existing feature selection approaches with respect to efficiency and effectiveness. As an example, genetic algorithm is an effective search algorithm that lends itself directly to feature selection; however this direct application is hindered by the recent increase of data dimensionality. Therefore adapting genetic algorithm to cope with the high dimensionality of the data becomes increasingly appealing. Approach: In this study, we proposed an adapted version of genetic algorithm that can be applied for feature selection in high dimensional data. The proposed approach is based essentially on a variable length representation scheme and a set of modified and proposed genetic operators. To assess the effectiveness of the proposed approach, we applied it for cues phrase selection and compared its performance with a number of ranking approaches which are always applied for this task. Results and Conclusion: The results provide experimental evidences on the effectiveness of the proposed approach for feature selection in high dimensional data.

Robust Dialog Management Through A Context-centric Architecture

Abstract: This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users.

Building dialogue systems that sell

Abstract: This paper addresses the application of dialogue management techniques in a buyer-seller situation. In particular, we will describe the techniques used in the SCHISMA system, a system that provides the caller with information on theatre performances, and optionally "sells" the caller one or more tickets for a given performance.

Chapter 8 Systems Engineering and Conversational Agents

Abstract: This chapter describes Conversational Agents (CAs) in the context of Systems Engineering. A CA is a computer program which interacts with a user through natural language dialogue and provides some form of service. CA technology has two points of interest to systems engineers: the use of systems engineering techniques in CA research and the application of CAs in project development. CAs offer the opportunity to automate more complex applications than are feasible with conventional web interfaces. Currently such applications require a human expert in the domain to mediate between the user and the application. The CA effectively replaces the human expert. This chapter reviews the current capabilities of various CA technologies, outlines a development methodology for systems engineering practitioners interested in developing real world applications and suggests a number of directions for systems engineers who wish to participate in CA research.