Jan 19, 2017 · Currently, convolutional neural network (CNN) is a state-of-the-art method for DDI extraction. One limitation of CNN is that it neglects long ...

Dep-CNN performs convolution operation on adjacent words in word sentences and dependency parsing trees of candidate DDIs instances.

This work proposes a dependency-based convolutional neural network (DCNN) for DDI extraction and designs a simple rule to combine CNN with DCNN, that is, ...

DCNN recognizes DDI extraction as a multi-class classification problem. It takes each pair of drugs in the same sentence and context of the drug pair as a ...

The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

May 25, 2017 · The current state-of-the-art for the extraction of DDIs is based on feature-engineering algorithms (such as support vector machines), which ...

We propose a novel DDI extraction method named Drug-drug Interactions extRaction with Enhanced Dependency Graph and Attention Mechanism in this work.

Dec 28, 2017 · In this paper, we propose a dependency-based bi-directional long short term memory network model for DDI extraction. In our model, three ...

In this article, we proposed a model that combines the graph convolution neural network (GCNN) and bidirectional long short-term memory (BiLSTM) to extract DDI ...

In this study, the general process of DDI relation extraction based on deep learning is introduced first for comprehensive analysis.