Comparing and combining chunkers of biomedical text

Text chunking is an essential pre-processing step in information extraction systems. No comparative studies of chunking systems, including sentence splitting, tokenization and part-of-speech tagging, are available for the biomedical domain. We compared the usability (ease of integration, speed, trainability) and performance of six state-of-the-art chunkers for the biomedical domain, and combined the chunker results in order to improve chunking performance. We investigated six frequently used chunkers: GATE chunker, Genia Tagger, Lingpipe, MetaMap, OpenNLP, and Yamcha. All chunkers were integrated into the Unstructured Information Management Architecture framework. The GENIA Treebank corpus was used for training and testing. Performance was assessed for noun-phrase and verb-phrase chunking. For both noun-phrase chunking and verb-phrase chunking, OpenNLP performed best (F-scores 89.7% and 95.7%, respectively), but differences with Genia Tagger and Yamcha were small. With respect to usability, Lingpipe and OpenNLP scored best. When combining the results of the chunkers by a simple voting scheme, the F-score of the combined system improved by 3.1 percentage point for noun phrases and 0.6 percentage point for verb phrases as compared to the best single chunker. Changing the voting threshold offered a simple way to obtain a system with high precision (and moderate recall) or high recall (and moderate precision). This study is the first to compare the performance of the whole chunking pipeline, and to combine different existing chunking systems. Several chunkers showed good performance, but OpenNLP scored best both in performance and usability. The combination of chunker results by a simple voting scheme can further improve performance and allows for different precision-recall settings.