The paper introduces the CoNLL-2003 shared task on language-independent named entity recognition, focusing on four types of named entities: persons, locations, organizations, and miscellaneous names. The task involved developing systems using training and test data for English and German, with a focus on incorporating unannotated data and external resources. The evaluation method used F$_{\beta=1}$ rate, and 16 systems participated, employing various machine learning techniques such as Maximum Entropy Models, Hidden Markov Models, and connectionist approaches. The best performance was achieved by a combined system that used Maximum Entropy Models, transformation-based learning, Hidden Markov Models, and robust risk minimization, along with gazetteers and externally trained named entity recognizers. The paper also discusses the impact of using additional information, such as gazetteers and unannotated data, on system performance.The paper introduces the CoNLL-2003 shared task on language-independent named entity recognition, focusing on four types of named entities: persons, locations, organizations, and miscellaneous names. The task involved developing systems using training and test data for English and German, with a focus on incorporating unannotated data and external resources. The evaluation method used F$_{\beta=1}$ rate, and 16 systems participated, employing various machine learning techniques such as Maximum Entropy Models, Hidden Markov Models, and connectionist approaches. The best performance was achieved by a combined system that used Maximum Entropy Models, transformation-based learning, Hidden Markov Models, and robust risk minimization, along with gazetteers and externally trained named entity recognizers. The paper also discusses the impact of using additional information, such as gazetteers and unannotated data, on system performance.