Chunking is a shallow form of parsing that identifies continuous spans of tokens that form syntactic units such as noun phrases or verb phrases.
The Penn Treebank is typically used for evaluating chunking. Sections 15-18 are used for training, section 19 for development, and and section 20 for testing. Models are evaluated based on F1.
|Model||F1 score||Paper / Source|
|JMT (Hashimoto et al., 2017)||95.77||A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks|
|Low supervision (Søgaard and Goldberg, 2016)||95.57||Deep multi-task learning with low level tasks supervised at lower layers|
|Suzuki and Isozaki (2008)||95.15||Semi-Supervised Sequential Labeling and Segmentation using Giga-word Scale Unlabeled Data|
|NCRF++ (Yang and Zhang, 2018)||95.06||NCRF++: An Open-source Neural Sequence Labeling Toolkit||NCRF++|