Selection of text feature item is a basic and important matter for text mining and information
retrieval. Traditional methods of feature extraction require handcrafted features. To hand …

Relation classification is an important semantic processing task in the field of natural
language processing (NLP). State-ofthe-art systems still rely on lexical resources such as …

This article reviews recent advances in applying natural language processing (NLP) to
Electronic Health Records (EHRs) for computational phenoty**. NLP-based …

In this paper, we present GraphRel, an end-to-end relation extraction model which uses
graph convolutional networks (GCNs) to jointly learn named entities and relations. In …

Entity and relation extraction is a task that combines detecting entity mentions and
recognizing entities' semantic relationships from unstructured text. We propose a hybrid …

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 …

We proposed the first models based on recurrent neural networks (more specifically Long
Short-Term Memory-LSTM) for classifying relations from clinical notes. We tested our models …

The explosive growth of the amount of information on Internet has made Web page
classification essential for Web information management, retrieval, and integration, Web …

Distant supervision is an effective method to generate large scale labeled data for relation
extraction, which assumes that if a pair of entities appears in some relation of a Knowledge …

Attention-based deep learning model as a human-centered smart technology has become
the state-of-the-art method in addressing relation extraction, while implementing natural …