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Extracting biomedical relations such as drug-drug interaction (DDI) from text is an important task in biomedical natural language processing. Due to the large number of complex sentences in biomedical literature, researchers have employed some sentence simplification techniques to improve the performance of the relation extraction methods. However, no significant improvement has been reported in literature, since the task is difficult. This paper aims to explore clause dependency related features alongside to linguisticbased negation scope and cues to overcome complexity of the sentences. The results show through employing the proposed features combined with a bag of words kernel, the performance of the used kernel methods improves. Moreover, experiments show that the enhanced local context kernel outperforms other methods. The proposed method can be used as an alternative approach for sentence simplification techniques in biomedical area which is an error-prone task.

Automatic extraction of Drug-Drug interaction from literature through detecting clause dependency and linguistic-based negation