Distantly supervised biomedical relation extraction using piecewise attentive convolutional neural network and reinforcement learning.

ABSTRACT:

Objective

There have been various methods to deal with the erroneous training data in distantly supervised relation extraction (RE), however, their performance is still far from satisfaction. We aimed to deal with the insufficient modeling problem on instance-label correlations for predicting biomedical relations using deep learning and reinforcement learning.

Materials and methods

In this study, a new computational model called piecewise attentive convolutional neural network and reinforcement learning (PACNN+RL) was proposed to perform RE on distantly supervised data generated from Unified Medical Language System with MEDLINE abstracts and benchmark datasets. In PACNN+RL, PACNN was introduced to encode semantic information of biomedical text, and the RL method with memory

SUBMITTER: Zhu T 

PROVIDER: S-EPMC8633639 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature