Class Meeting for 10-707 11/2/2009
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This is one of the class meetings on the schedule for the course Information Extraction 10-707 in Fall 2009.
Overview of Bootstrapping and KnowItAll
Required Readings
Optional Readings
- Expanding the recall of relation extraction by bootstrapping, by J. Tomita, S. S.O Etzioni. In Adaptive Text Extraction and Mining (ATEM 2006), 2006.
- Weakly-supervised discovery of named entities using web search queries, by M. Pasca, {{{coauthors}}}. In CIKM '07, 2007.
- Scaling textual inference to the Web, by S. Schoenmackers, O. Etzioni, D. S Weld, T. Center. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, 2008.
- Toward Completeness in Concept Extraction and Classification, by E. Hovy, Z. Kozareva, E. Riloff. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009.
- Updating a Name Tagger Using Contemporary Unlabeled Data, by C. Mota, R. Grishman. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, 2009.
- Web-scale distributional similarity and entity set expansion. By P. Pantel, E. Crestan, A. Borkovsky, A. M Popescu, V. Vyas. In Proceedings of EMNLP-09, Singapore, vol. {{{volume}}} ({{{issue}}}), 2009.
- Semi-Supervised Active Learning for Sequence Labeling, by K. Tomanek, U. Hahn. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2009.
- Unsupervised Relation Extraction by Mining Wikipedia Texts Using Information from the Web, by Y. Yan, N. Okazaki, Y. Matsuo, Z. Yang, M. Ishizuka. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP, 2009.. Combines dependency patterns in parse wikipedia text and bootstrapping-style surface patterns on Web text.
- A context pattern induction method for named entity extraction, by P. P Talukdar, T. Brants, M. L.F Pereira. In Tenth Conference on Computational Natural Language Learning, {{{date}}}..