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

ABSTRACT:

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 DDI extraction method. In this method, a novel word embedding, syntax word embedding, is proposed to employ the syntactic information of a sentence. Then the position and part of speech features are introduced to extend the embedding of each word. Later, auto-encoder is introduced to encode the traditional bag-of-words feature (sparse 0-1 vector) as the dense real value vector. Finally,

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC5181565 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature