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

Extracting biomedical relations such as drug-drug interaction (DDI) from text is an important task in biomedical NLP. 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, due to difficulty of the task, there is no noteworthy improvement in the research literature. This paper aims to explore clause dependency related features alongside to linguistic-based negation scope and cues to overcome complexity of the sentences. The experiments indicate the ratio of negation cues which is another source of inaccuracy is higher in complex sentences in comparison with simple ones. Additionally, the results show by employing the proposed features combined with a bag of words kernel, the performance of the used kernel methods improves. Moreover, experiments show 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.