University thesis:
Universität Hildesheim, Institut für Informatik, Tagungsbeitrag: 2006
Footnote:
Description:
Information on the internet is a vast resource for question answering. As the amount of available information from web pages increases, novel methods for finding precise answers to user queries and questions must be found. Standard information retrieval methods are efficient, but often fail to provide a user with short, precise answers. A deep linguistic analysis of all information is time consuming, but it offers more advanced means to find answers to a user’s question. Shallow natural language processing methods seem to work well on a limited range of questions, but they are not suitable for finding answers to more complex questions. This paper describes work in progress on the question answering system IRSAW (Intelligent Information Retrieval on the Basis of a Semantically Annotated Web), a system that combines information retrieval with a deep linguistic analysis of texts to obtain answers to natural language questions. In IRSAW, different techniques for finding answers lead to different sets of answer candidates, which are then merged to produce a final answer. The system’s architecture and functionality are described before evaluation results of a first prototype are presented.