We present a novel framework for automated extraction and approximation of numerical object attributes such as height and weight from the Web. Given an object-attribute pair, we discover and analyze attribute information for a set of comparable objects in order to infer the desired value. This allows us to approximate the desired numerical values even when no exact values can be found in the text. Our framework makes use of relation defining patterns and WordNet similarity information. First, we obtain from the Web and WordNet a list of terms similar to the given object. Then we retrieve attribute values for each term in this list, and information that allows us to compare different objects in the list and to infer the attribute value range. Finally, we combine the retrieved data for all terms from the list to select or approximate the requested value. We evaluate our method using automated question answering, WordNet enrichment, and comparison with answers given in Wikipedia and by leading search engines. In all of these, our framework provides a significant improvement.
|Title of host publication
|ACL 2010 - 48th Annual Meeting of the Association for Computational Linguistics, Conference Proceedings
|Jan Hajic, Sandra Carberry, Stephen Clark
|Association for Computational Linguistics (ACL)
|Number of pages
|Published - 2010
|48th Annual Meeting of the Association for Computational Linguistics, ACL 2010 - Uppsala, Sweden
Duration: 11 Jul 2010 → 16 Jul 2010
|Proceedings of the Annual Meeting of the Association for Computational Linguistics
|48th Annual Meeting of the Association for Computational Linguistics, ACL 2010
|11/07/10 → 16/07/10
Bibliographical notePublisher Copyright:
© 2010 Association for Computational Linguistics.