Umar Maqsud, Sebastian Arnold, Michael Hülfenhaus and Alan Akbik
Nerdle: Topic-Specific Question Answering Using Wikia Seeds
COLING 2014 Conference in Dublin, Ireland
Abstract:
The WIKIA project maintains wikis across a diverse range of subjects from areas of popular culture. Each wiki consists of collaboratively authored content and focuses on a particular topic, including franchises such as “Star Trek”, “Star Wars” and “The Simpsons”. In this paper, we investigate the use of such wikis to create Question-Answering (QA) systems for a given topic. Our key idea is to use a wiki as seed to gather large amounts of relevant text and to use semantic role labeling (SRL) methods to extract N-ary facts from this data. By applying our method to very large amounts of topically focused text, we propose to address the coverage issues that have been noted for QA systems built using such techniques. To illustrate the strengths and weaknesses of the proposed approach, we make a Web demonstrator of our system publicly available; it provides a QA view that enables users to pose natural language questions to the system and that visualizes how questions are interpreted and matched to answers. In addition, the demonstrator provides a graph exploration view in which users can directly browse the fact base in order to inspect the scope of the extracted information.
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