Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition.
Tjong Kim Sang, Erik F. and De Meulder, Fien. Proceedings of CoNLL-2003. 2003. 142--147.

Memory-Based Named Entity Recognition using Unannotated Data.
De Meulder, Fien and Walter Daelemans.
Proceedings of CoNLL-2003. 2003. 208--211.

Named Entity Recognition with Character-Level Models.
Dan Klein and Joseph Smarr and Huy Nguyen and Christopher D. Manning.
Proceedings of CoNLL-2003. 2003. 180--183.

Named Entity Recognition through Classifier Combination.
Radu Florian and Abe Ittycheriah and Hongyan Jing and Tong Zhang.
Proceedings of CoNLL-2003. 2003. 168--171.

Named Entity Recognition with a Maximum Entropy Approach.
Chieu, Hai Leong and Ng, Hwee Tou.
Proceedings of CoNLL-2003. 2003. 160--163.

Learning to Recognize Names Across Languages.
A. Gallippi.
acl96. Santa Cruz, California, USA. 1996.

Nymble: a High-Performance Learning Name-finder.
D. M. Bikel and S. Miller and R. Schwartz and R. Weischedel.
anlp97. Washington, USA. March 1997.

An Intelligent Text Extraction and Navigation System.
J. Piskorski and G. Neumann. Proceedings of the 6th RIAO. April 2000.

A Maximum Entropy Approach to Named Entity Recognition.
A. Borthwick. 1999.

Named entity (NE) recognition is a form of information extraction in which the major task is to identify and classify from NL text every word or sequence of words as being a person-name, organizaton, location, date, time, monetary value, percentage expression. NE recognition has a high impact for a number of applications, like e.g., InterNet search engines, text data mining or answer extraction.