Automatic issue extraction from a focused dialogue

Various methodologies for structuring the process of domain modeling have been proposed, but there are few software tools that provide automatic support for the process of constructing a domain model. The problem is that it is hard to extract the relevant concepts from natural language texts since these typically include many irrelevant details that are hard to discern from relevant concepts. In this paper, we propose an alternative approach to extract domain models from natural language input. The idea is that more effective, automatic extraction is possible from a natural language text that is produced in a focused dialogue game. We present an application of this idea in the area of pre-negotiation, in combination with sophisticated parsing and transduction techniques for natural language and fairly simple pattern matching rules. Furthermore, a prototype is presented of a conversation-oriented experimentation environment for cooperative conceptualization. Several experiments have been performed to evaluate the approach and environment, and a technique for measuring the quality of extraction has been defined. The experiments indicate that even with a simple implementation of the proposed approach reasonably acceptable results can be obtained.