The paper introduces the CoNLL-2000 shared task on text chunking, which involves dividing text into syntactically related, non-overlapping groups of words. The authors provide background information on the datasets used, describe the systems that participated in the task, and discuss their performance. The task is particularly useful for preprocessing in parsing tasks, with a focus on identifying phrases of various syntactic categories. The chunk types are based on the syntactic category labels in the Penn Treebank II corpus, and the paper details the challenges and complexities in converting trees into chunks. The data sets used for the task include WSJ sections 15-18 for training and section 20 for testing, with chunks represented using specific tags. The performance of eleven systems is evaluated using precision, recall, and Fβ=1 rates, with the best results achieved by a combination of Support Vector Machines. The paper also reviews related work and concludes by highlighting the significance of the task and the contributions of the participating systems.The paper introduces the CoNLL-2000 shared task on text chunking, which involves dividing text into syntactically related, non-overlapping groups of words. The authors provide background information on the datasets used, describe the systems that participated in the task, and discuss their performance. The task is particularly useful for preprocessing in parsing tasks, with a focus on identifying phrases of various syntactic categories. The chunk types are based on the syntactic category labels in the Penn Treebank II corpus, and the paper details the challenges and complexities in converting trees into chunks. The data sets used for the task include WSJ sections 15-18 for training and section 20 for testing, with chunks represented using specific tags. The performance of eleven systems is evaluated using precision, recall, and Fβ=1 rates, with the best results achieved by a combination of Support Vector Machines. The paper also reviews related work and concludes by highlighting the significance of the task and the contributions of the participating systems.