Abstract. 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 impoverished when measured on various genres of scientific and technical writing, and that this significantly impacts parse accuracy. To resolve this without requiring in-domain treebank data, we show how existing domain-specific lexical resources may be leveraged to augment PTB-training: part-of-speech tags, dictionary collocations, and namedentities. Using a state-of-the-art statistical parser [3] as our baseline, our lexically-adapted parser achieves a 14.2% reduction in error. With oracleknowledge of named-entities, this error reduction improves to 21.2%.
REF utilized POS tags, dictionary collocations, and named entities for parser adaptation, and then succeeded to achieve accuracy improvement.
Parsing Biomedical Literature
Second International Joint Conference on Natural Language Processing: Full Papers
Computer Science