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A Pattern Learning Approach to Question Answering Within the Ephyra Framework

Nico SchlaeferContact Information, Petra GieselmannContact Information, Thomas SchaafContact Information and Alex Waibel1, 2 Contact Information

(1)  Interactive Systems Labs, ITI, Universität Karlsruhe, Am Fasanengarten 5, 76131 Karlsruhe, Germany
(2)  Interactive Systems Labs, Carnegie Mellon University, 407 S. Craig Street, Pittsburgh, PA 15213,  
Abstract
This paper describes the Ephyra question answering engine, a modular and extensible framework that allows to integrate multiple approaches to question answering in one system. Our framework can be adapted to languages other than English by replacing language-specific components. It supports the two major approaches to question answering, knowledge annotation and knowledge mining. Ephyra uses the web as a data resource, but could also work with smaller corpora. In addition, we propose a novel approach to question interpretation which abstracts from the original formulation of the question. Text patterns are used to interpret a question and to extract answers from text snippets. Our system automatically learns the patterns for answer extraction, using question-answer pairs as training data. Experimental results revealed the potential of this approach.

Contact Information Nico Schlaefer
Email: nico@ira.uka.de

Contact Information Petra Gieselmann
Email: petra@ira.uka.de

Contact Information Thomas Schaaf
Email: tschaaf@cs.cmu.edu

Contact Information Alex Waibel
Email: waibel@cs.cmu.edu
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