Extraction of entity relations from Chinese medical literature based on multi-scale CRNN.

ABSTRACT:

Background

Entity relation extraction technology can be used to extract entities and relations from medical literature, and automatically establish professional mapping knowledge domains. The classical text classification model, convolutional neural networks for sentence classification (TEXTCNN), has been shown to have good classification performance, but also has a long-distance dependency problem, which is a common problem of convolutional neural networks (CNNs). Recurrent neural networks (RNN) address the long-distance dependency problem but cannot capture text features at a specific scale in the text.

Methods

To solve these problems, this study sought to establish a model with a multi-scale convolutional recurrent neural network for Sentence Classification (TEXTCRNN) to

SUBMITTER: Chen T 

PROVIDER: S-EPMC9347033 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature