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 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%.
We would like to thank the National Science Foundation for their support of this work (IIS-0112432, LIS-9721276, and DMS-0074276), as well as thank Sharon Goldwater and our anonymous reviewers for their valuable feeback.
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Lease, M., Charniak, E. (2005). Parsing Biomedical Literature. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_6
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DOI: https://doi.org/10.1007/11562214_6
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