Abstract
As the number of published papers on proteins increases rapidly, manual protein annotation for biological sequence databases faces the problem of catching up with the speed of publication. Automated information extraction for protein annotation offers a solution to this problem. Generally, information extraction tasks have relied on the availability of pre-defined templates as well as annotated corpora. However, in many real world applications, it is difficult to fulfill this requirement; only relevant sentences for target domains can be easily collected. At the same time, other resources can be harnessed to compensate for this difficulty: natural language processing provides reliable tools for syntactic text analysis, and in bio-medical domains, there is a large amount of background knowledge available, e.g., in the form of ontologies. In this paper, we present a method for learning information extraction rules without pre-defined templates or labor-intensive pre-annotation by exploiting various types of background knowledge in an inductive logic programming framework.
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Kim, JH., Hilario, M. (2005). Learning Information Extraction Rules for Protein Annotation from Unannotated Corpora. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2005. Lecture Notes in Computer Science, vol 3406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30586-6_56
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DOI: https://doi.org/10.1007/978-3-540-30586-6_56
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