توجه: محتویات این صفحه به صورت خودکار پردازش شده و مقاله‌های نویسندگانی با تشابه اسمی، همگی در بخش یکسان نمایش داده می‌شوند.
۱Automatic extraction of Drug–Drug interaction from literature through detecting clause dependency and linguistic–based negation
نویسنده(ها): ،
اطلاعات انتشار: Journal of Artificial Intelligence and Data Mining، چهارم،شماره۲، ۲۰۱۶، سال
تعداد صفحات: ۱۰
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 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.
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