Enhancing Extraction of Drug-Drug Interaction from Literature Using Neutral Candidates, Negation, and Clause Dependency

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Enhancing Extraction of Drug-Drug Interaction from Literature Using Neutral Candidates, Negation, and Clause Dependency

MOTIVATION Supervised biomedical relation extraction plays an important role in biomedical natural language processing, endeavoring to obtain the relations between biomedical entities. Drug-drug interactions, which are investigated in the present paper, are notably among the critical biomedical relations. Thus far many methods have been developed with the aim of extracting DDI relations. Howeve...

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Extraction of Drug-Drug Interaction from Literature through Detecting Linguistic-based Negation and Clause Dependency

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 t...

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Extracting Drug-Drug interaction from text using negation features

Extracting biomedical relations from text is an important task in BioMedical NLP. There are several systems developed for this purpose but the ones on Drug-Drug interactions are still a few. In this paper we want to show the effectiveness of negation features for this task. We firstly describe how we extended the DrugDDI corpus by annotating it with the scope of negation, and secondly we report...

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Drug drug interaction extraction from biomedical literature using syntax convolutional neural network

MOTIVATION Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve. RESULTS In this article, we present a syntax convolutional neural network (SCNN) based ...

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ژورنال

عنوان ژورنال: PLOS ONE

سال: 2016

ISSN: 1932-6203

DOI: 10.1371/journal.pone.0163480