1. Borthwick, A.E.: A Maximum Entropy Approach to Named Entity Recognition. Ph.D. thesis, New York, NY, USA. AAI9945252 (1999)
2. Lafferty, J.D., McCallum, A., Pereira, F.C.N.: Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of the Eighteenth International Conference on Machine Learning. ICML 2001, San Francisco, CA, USA, pp. 282–289. Morgan Kaufmann Publishers Inc. (2001)
3. Bikel, D.M., Miller, S., Schwartz, R., Weischedel, R.: Nymble: a high-performance learning name-finder. In: Proceedings of the Fifth Conference on Applied Natural Language Processing. ANLC 1997, Stroudsburg, PA, USA, pp. 194–201. Association for Computational Linguistics (1997)
4. Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 100–110 (1999)
5. Béchet, F., Nasr, A., Genet, F.: Tagging unknown proper names using decision trees. In: Proceedings of the 38th Annual Meeting on Association for Computational Linguistics. ACL 2000, Stroudsburg, PA, USA, pp. 77–84. Association for Computational Linguistics (2000)