Difference between revisions of "Class Meeting for 10-707 10/19/2009"
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Latest revision as of 10:42, 3 September 2010
This is one of the class meetings on the schedule for the course Information Extraction 10-707 in Fall 2009.
Kernels and Relation Extraction
Required Readings
- Learning to extract relations from the web using minimal supervision, by R. Bunescu, R. Mooney. In ANNUAL MEETING-ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, 2007.
- Semantic role labelling with tree conditional random fields, by T. Cohn, P. Blunsom. In Ninth Conference on Computational Natural Language Learning, 2005.
Optional Readings
- Hierarchical Hidden Markov Models for Information Extraction, Skounakis, Craven and Ray, IJCAI 2003. An HMM-based approach to relation extraction.
- Exploring syntactic features for relation extraction using a convolution tree kernel, by Min Zhang, Jie Zhang, Jian Su. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, 2006.. Bettering the Bunescu and Mooney kernel-based approach with a new kernel.
- Autonomously semantifying wikipedia, by Fei Wu, Daniel S. Weld. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, 2007.. Applying NER techniques to Wikipedia using infobox data as training.
- Amplifying community content creation with mixed initiative information extraction, by Raphael Hoffmann, Saleema Amershi, Kayur Patel, Fei Wu, James Fogarty, Daniel S. Weld. In Proceedings of the 27th International Conference on Human Factors in Computing Systems, 2009.. User interface issues involved in getting the We and Weld 2007 ideas to really work.
- Nested Named Entity Recognition, by J. R Finkel, C. D Manning. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009..
- Structured prediction models via the matrix-tree theorem, by T. Koo, A. Globerson, X. Carreras, M. Collins. In Proc. EMNLP, 2007.. Very general alternative approach to learning structured outputs based on EG. Difficult paper but worth it.
