Impacts of Features and Tagging Schemes on Chunking

Text chunking, also known as shallow parsing, is an important task in natural language processing, and very useful for other tasks. By means of discriminate machine learning methods and extensive experiments, this paper investigates the impacts of different tagging schemes and feature types on chunking efficiency and effectiveness on corpora with different chunk specifications and languages. We find out that it costs more time for training and tagging with the machine learning method with more features and more fine-grained tagging schemes on all the corpora. Nevertheless, the tagging time is less affected by them. It is also revealed from our investigation that the method with more features and more fine-grained tagging schemes has better performance, but the chunk specification of corpus may have impacts on the choice.