Abstract
Free text botanical descriptions contained in printed floras can provide a wealth of valuable scientific information. In spite of this richness, these texts have seldom been analyzed on a large scale using NLP techniques. To fill this gap, we describe how we managed to extract a set of terminological resources by parsing a large corpus of botanical texts. The tools and techniques used are presented as well as the rationale for favoring a deep parsing approach coupled with error mining methods over a simple pattern matching approach.
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Role, F., Fernandez Gavilanes, M., Villemonte de la Clergerie, É. (2007). Large-Scale Knowledge Acquisition from Botanical Texts. In: Kedad, Z., Lammari, N., Métais, E., Meziane, F., Rezgui, Y. (eds) Natural Language Processing and Information Systems. NLDB 2007. Lecture Notes in Computer Science, vol 4592. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73351-5_36
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DOI: https://doi.org/10.1007/978-3-540-73351-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73350-8
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