  A recent study reported development of Muscorian, a generic text processing
tool for extracting protein-protein interactions from text that achieved
comparable performance to biomedical-specific text processing tools. This
result was unexpected since potential errors from a series of text analysis
processes is likely to adversely affect the outcome of the entire process. Most
biomedical entity relationship extraction tools have used biomedical-specific
parts-of-speech (POS) tagger as errors in POS tagging and are likely to affect
subsequent semantic analysis of the text, such as shallow parsing. This study
aims to evaluate the parts-of-speech (POS) tagging accuracy and attempts to
explore whether a comparable performance is obtained when a generic POS tagger,
MontyTagger, was used in place of MedPost, a tagger trained in biomedical text.
Our results demonstrated that MontyTagger, Muscorian's POS tagger, has a POS
tagging accuracy of 83.1% when tested on biomedical text. Replacing MontyTagger
with MedPost did not result in a significant improvement in entity relationship
extraction from text; precision of 55.6% from MontyTagger versus 56.8% from
MedPost on directional relationships and 86.1% from MontyTagger compared to
81.8% from MedPost on nondirectional relationships. This is unexpected as the
potential for poor POS tagging by MontyTagger is likely to affect the outcome
of the information extraction. An analysis of POS tagging errors demonstrated
that 78.5% of tagging errors are being compensated by shallow parsing. Thus,
despite 83.1% tagging accuracy, MontyTagger has a functional tagging accuracy
of 94.6%.
