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Updated: Aug 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Edge Weight Updating Neural Network for Named Entity Normalization.

Sung Hwan Jeon¹, Sungzoon Cho^1,2

¹Department of Industrial Engineering, Seoul National University, 1, Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea.

Neural Processing Letters

|December 27, 2022

Summary

This summary is machine-generated.

This study introduces a novel neural network for named entity normalization, significantly improving accuracy in bioinformatics and financial text mining. The model achieved state-of-the-art results across multiple datasets.

Keywords:

Edge weight updating neural network Named entity graph Named entity normalization Text mining in bioinformatics Text mining in finance

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Area of Science:

Computational Biology
Natural Language Processing
Bioinformatics

Background:

Accurate named entity normalization is crucial for effective text mining.
Improved normalization enhances downstream text analytic applications.

Purpose of the Study:

To develop a novel named entity normalization model.
To validate the model's performance on diverse datasets.

Main Methods:

A novel edge weight updating neural network was developed.
Model performance was evaluated on NCBI disease, BC5CDR disease, BC5CDR chemical, and a financial dataset.

Main Results:

The model achieved the highest performance across various evaluation metrics.
State-of-the-art results were obtained on four distinct datasets.

Conclusions:

The proposed neural network model significantly advances named entity normalization.
The model demonstrates efficacy in both specialized bioinformatics and general financial applications.