The CoNLL-2000 shared task focused on text chunking, which involves dividing text into non-overlapping groups of words that are syntactically related. The task aimed to address the lack of annotated corpora for this purpose, using the Wall Street Journal (WSJ) part of the Penn Treebank II corpus. The task involved identifying various chunk types, including noun phrases (NPs), verb phrases (VPs), adverbial phrases (ADVPs), adjective phrases (ADJPs), prepositional phrases (PPs), and others. Each chunk type was defined based on syntactic categories and their relationships within the tree structure. The task used a standard POS tagger to generate POS tags for tokens, which were then used to evaluate chunking performance. The data included training and test sets, with the test set consisting of WSJ section 20. The evaluation was based on precision, recall, and the Fβ=1 score, which is a harmonic mean of precision and recall. Several systems participated in the task, including rule-based, memory-based, statistical, and combined systems. The best performing system was a combination of support vector machines (SVMs) submitted by Taku Kudoh and Yuji Matsumoto, achieving an Fβ=1 score of 93.48. The task highlighted the importance of chunking in natural language processing and demonstrated the effectiveness of various machine learning approaches in this domain. The results showed that most systems outperformed a baseline approach, with many achieving high Fβ=1 scores. The task also emphasized the need for further research into chunking and its applications in NLP.The CoNLL-2000 shared task focused on text chunking, which involves dividing text into non-overlapping groups of words that are syntactically related. The task aimed to address the lack of annotated corpora for this purpose, using the Wall Street Journal (WSJ) part of the Penn Treebank II corpus. The task involved identifying various chunk types, including noun phrases (NPs), verb phrases (VPs), adverbial phrases (ADVPs), adjective phrases (ADJPs), prepositional phrases (PPs), and others. Each chunk type was defined based on syntactic categories and their relationships within the tree structure. The task used a standard POS tagger to generate POS tags for tokens, which were then used to evaluate chunking performance. The data included training and test sets, with the test set consisting of WSJ section 20. The evaluation was based on precision, recall, and the Fβ=1 score, which is a harmonic mean of precision and recall. Several systems participated in the task, including rule-based, memory-based, statistical, and combined systems. The best performing system was a combination of support vector machines (SVMs) submitted by Taku Kudoh and Yuji Matsumoto, achieving an Fβ=1 score of 93.48. The task highlighted the importance of chunking in natural language processing and demonstrated the effectiveness of various machine learning approaches in this domain. The results showed that most systems outperformed a baseline approach, with many achieving high Fβ=1 scores. The task also emphasized the need for further research into chunking and its applications in NLP.