Computational Geometry (Machine intelligence and pattern by Godfried T. Toussaint

By Godfried T. Toussaint

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In Proceedings of the 3rd Asia Information Retrieval Symposium, pages 54–66, 2006. [35] Hideki Isozaki and Hideto Kazawa. Efficient support vector classifiers for named entity recognition. In Proceedings of the 19th International Conference on Computational Linguistics, 2002. [36] Jing Jiang and ChengXiang Zhai. Exploiting domain structure for named entity recognition. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pages 74–81, 2006.

They assume that if two entities participate in a relation, any sentence that contain these two entities express that relation. Because this assumption does not always hold, Mintz et al. use features extracted from different sentences containing the entity pair to create a richer feature vector that is supposed to be more reliable. They define lexical, syntactic and named entity tag features. They use standard multi-class logistic regression as the classification algorithm. Their experiments show that this method can reach almost 70% of precision based on human judgment.

Semi-markov conditional random fields for information extraction. In Advances in Neural Information Processing Systems 17, pages 1185–1192. 2005. Burr Settles. Biomedical named entity recognition using conditional random fields and rich feature sets. In Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and Its Applications, pages 104–107, 2004. Yusuke Shinyama and Satoshi Sekine. Preemptive information extraction using unrestricted relation discovery. In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, pages 304–311, 2006.

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