Due to the rapid growth in biological technology, the development of high-quality information extraction systems is needed and still remains a challenge. Several recently proposed approaches to biological relation extraction are based on machine learning techniques on lexical and syntactic information. Most use the dependency path between two genes/proteins instead of the whole dependency tree of a sentence for identifying relationships. However, the dependency path may not have any node between two entities. If a limited set of annotated training corpora is used for the construction of tree information of biological relationships, the training corpus will lack some sentence structures and cannot predict whether the sentence has a biological relationship. In this paper, we developed a biological relation extraction system called Evolutional Tree Extraction System ¨C ETree. We extended the dependency path to the dependency subtree and developed a method that can automatically expand and prune these existing dependency subtrees into various dependency subtrees. These dependency subtrees are called ¨DEvolutional Trees¡¬ and are used to predict the biological relationship sentences.
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