Paper: Parsing Biomedical Literature

ACL ID I05-1006
Title Parsing Biomedical Literature
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

We present a preliminary study of several parser adaptation techniques evaluated on the GENIA corpus of MEDLINE abstracts [1,2]. We begin by observing that the Penn Treebank (PTB) is lexically im- poverished when measured on various genres of scientific and techni- cal writing, and that this significantly impacts parse accuracy. To re- solve this without requiring in-domain treebank data, we show how ex- isting domain-specific lexical resources may be leveraged to augment PTB-training: part-of-speech tags, dictionary collocations, and named- entities. Using a state-of-the-art statistical parser [3] as our baseline, our lexically-adapted parser achieves a 14.2% reduction in error. With oracle- knowledge of named-entities, this error reduction improves to 21.2%.