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A Pattern Learning Approach to Question Answering Within the Ephyra Framework
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IV Dialogue
A Pattern Learning Approach to Question Answering Within the Ephyra Framework
Nico Schlaefer1 , Petra Gieselmann1 , Thomas Schaaf2 and Alex Waibel1, 2 
| (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.
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