A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study.

ABSTRACT:

Background

The normalization of institution names is of great importance for literature retrieval, statistics of academic achievements, and evaluation of the competitiveness of research institutions. Differences in authors' writing habits and spelling mistakes lead to various names of institutions, which affects the analysis of publication data. With the development of deep learning models and the increasing maturity of natural language processing methods, training a deep learning-based institution name normalization model can increase the accuracy of institution name normalization at the semantic level.

Objective

This study aimed to train a deep learning-based model for institution name normalization based on the feature fusion of affiliation data from multisource literatur

SUBMITTER: Chen Y 

PROVIDER: S-EPMC10474509 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature