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Learning Relations from Biomedical Corpora Using Dependency Tree Levels

In this paper we address the problem of learning relations in the biomedical domain. We propose a representation which takes into account the syntactic information and allows for using different machine learning methods. The results we have obtained are comparable to the performance of relation learning systems in the biomedical domain and in some cases out-perform them. In addition, we have studied the impact of ensemble methods on learning relations using the representation we proposed. Given that recall is very important for relation learning, we have studied how it can be improved. It has been shown that ensemble methods techniques provide recall of 76,2% (with precision of 69,4%).