An initial study of full parsing of clinical text using the Stanford Parser

Abstract

Full parsing recognizes a sentence and generates a syntactic structure of it (a parse tree), which is useful for many natural language processing (NLP) applications. The Stanford Parser is one of the state-of-art parsers in the general English domain. However, there is no formal evaluation of its performance in clinical text that often contains ungrammatical structures. In this study, we randomly selected 50 sentences in the clinical corpus from 2010 i2b2 NLP challenge and manually annotated them to create a gold standard of parse trees. Our evaluation showed that the original Stanford Parser achieved a bracketing F-measure (BF) of 77% on the gold standard. Moreover, we assessed the effect of part-of-speech (POS) tags on parsing and our results showed that manually corrected POS tags achieved a maximum BF of 81%. Furthermore, we analyzed errors of the Stanford Parser and provided valuable insights to large-scale parse tree annotation for clinical text.