The paper discusses two key tasks performed by a Question Answering Dialogue System (QADS): user question interpretation and answer extraction. The system represents an interactive quiz game application. The information that forms the content of the game is concerned with biographical facts of famous people's life. The process of a question classification and answer extraction is performed based on a domain-specific taxonomy of semantic roles and relations computing the Expected Answer Type (EAT). Question interpretation is achieved performing a sequence of classification, information extraction, query formalization and query expansion tasks. The expanded query facilitates the search and retrieval of the information. The facts are extracted from Wikipedia pages by means of the same set of semantic relations, whose fillers are identified by trained sequence classifiers and pattern matching tools, and edited to be returned to the player as full-fledged system answers. The results (precision of 85% for the EAT classification of both in questions and answers) show that the presented approach fits the data well and can be considered as a promising method for other QA domains, in particular when dealing with unstructured information.
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