Abstract
This paper presents a part-of-speech tagger which is specifically tuned for biomedical text. We have built the tagger with maximum entropy modeling and a state-of-the-art tagging algorithm. The tagger was trained on a corpus containing newspaper articles and biomedical documents so that it would work well on various types of biomedical text. Experimental results on the Wall Street Journal corpus, the GENIA corpus, and the PennBioIE corpus revealed that adding training data from a different domain does not hurt the performance of a tagger, and our tagger exhibits very good precision (97% to 98%) on all these corpora. We also evaluated the robustness of the tagger using recent MEDLINE articles.
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Tsuruoka, Y. et al. (2005). Developing a Robust Part-of-Speech Tagger for Biomedical Text. In: Bozanis, P., Houstis, E.N. (eds) Advances in Informatics. PCI 2005. Lecture Notes in Computer Science, vol 3746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573036_36
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DOI: https://doi.org/10.1007/11573036_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29673-7
Online ISBN: 978-3-540-32091-3
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