Abstract
In this paper we present different ways to improve a basic machine learning approach to identify relations between biological named entities as annotated in the Genia corpus.
The main difficulty with learning from the Genia event-annotated corpus is the small amount of examples that are available for each relation type. We compare different ways to address the data sparseness problem: using the corpus as the initial seed of a bootstrapping procedure, generalizing classes of relations via the Genia ontology and generalizing classes via clustering.
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Alonso i Alemany, L., Bruno, S. (2009). Learning to Learn Biological Relations from a Small Training Set. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2009. Lecture Notes in Computer Science, vol 5449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00382-0_34
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DOI: https://doi.org/10.1007/978-3-642-00382-0_34
Publisher Name: Springer, Berlin, Heidelberg
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