Dialog Navigator: A Navigation System from Vague Questions to Specific Answers based on Real-World Text Collections

As computers and their networks continue to be developed, our day-to-day lives are being surrounded by increasingly more complex instruments, and we often have to ask questions about using them. At the same time, large collections of texts to answer these questions are being gathered. Therefore, there are potential answers to many of our questions that exist as texts somewhere. However, there are various gaps between our various questions and the texts, and these prevent us from accessing appropriate texts to answer our questions. The gaps are mainly composed of both expression and vagueness gaps. When we seek texts for answers using conventional keyword-based text retrieval systems, we often have trouble locating them. In contrast, when we ask experts on instruments or operators of call centers, they can resolve the various gaps, by interpreting our questions flexibly, and by producing some ask-backs. The problem with experts and call centers is that they are not always available. Two approaches have been studied to resolve the various gaps: the extension of keyword-based text retrieval systems, and the application of artificial intelligence techniques. However, these approaches have their respective limitations. The former uses texts or keywords as methods for ask-back questions, but these methods are not always suitable. The latter requires a specialized knowledge base described in formal languages, so it cannot be applied to existing collections with large amount of texts. This thesis targets real-world the large text collections provided by Microsoft Corporation, and addresses a novel methodology to resolve the gaps between various user questions and the texts. The methodology consists of two key solutions: precise and flexible methods of matching user questions with texts based on NLP (natural language processing) techniques, and ask-back methods using the matching methods. First, the matching methods, including sentence structure analysis and expression gap resolution, are described. In addition, these methods are extended into matching through metonymy, which is frequently observed in natural languages. After that, a solution to make ask-