Class Meeting for 10-707 10/25/2010
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This is one of the class meetings on the schedule for the course Information Extraction 10-707 in Fall 2010.
The TextRunner system
Required Readings
- Open information extraction from the web, by M. Banko, M. J Cafarella, S. Soderland, M. Broadhead, O. Etzioni. In Procs. of IJCAI, 2007.. About TextRunner.
Optional Readings
- Outclassing Wikipedia in Open-Domain Information Extraction: Weakly-Supervised Acquisition of Attributes over Conceptual Hierarchies, by M. Pasca, {{{coauthors}}}. In Proceedings of the 12th Conference of the European Chapter of the ACL, 2009.
- A Unified Model of Phrasal and Sentential Evidence for Information Extraction, by S. Patwardhan, E. Riloff. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing,, 2009.
- Using structured text for large-scale attribute extraction, by S. Ravi, M. PaÅŸca. In Proc CIKM 2008, 2008.
- Corroborate and learn facts from the web, by Shubin Zhao, Jonathan Betz. In KDD '07: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, 2007.
- Li, S., C. Y Lin, Y. I Song, and Z. Li. n.d. Comparable Entity Mining from Comparative Questions. ACL 2010
- Wu, F., and D. S Weld. 2010. Open information extraction using wikipedia. In The Annual Meeting of the Association for Computational Linguistics (ACL-2010). A text-runner like system trained on dependence-parsed wikipedia pages.
- Ittoo, A., and G. Bouma. n.d. On Learning Subtypes of the Part-Whole Relation: Do Not Mix your Seeds. ACL-2010. Some interesting analysis of experiments in bootstrapping to learn part-whole relations.
- Schoenmackers, S., J. Davis, O. Etzioni, and D. S Weld. 2010. Learning first-order horn clauses from web text. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP). Learning (simple) first-order relationships using data produced by TextRunner.
- Schoenmackers, S., J. Davis, O. Etzioni, and D. S Weld. 2010. Learning first-order horn clauses from web text. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP). Predicts the "functionality" of relations extracted from web text.
