Named Entity Normalization Model Using Edge Weight Updating Neural Network: Assimilation Between Knowledge-Driven Graph and Data-Driven Graph

TL;DR

This paper introduces a novel Edge Weight Updating Neural Network for named entity normalization, achieving state-of-the-art results across multiple datasets, including biomedical and financial domains.

Contribution

The paper proposes a new neural network model that effectively combines knowledge-driven and data-driven graphs for improved named entity normalization.

Findings

Achieved state-of-the-art performance on four datasets.

Validated model's effectiveness on biomedical and financial datasets.

Outperformed existing methods in various evaluation metrics.

Abstract

Discriminating the matched named entity pairs or identifying the entities' canonical forms are critical in text mining tasks. More precise named entity normalization in text mining will benefit other subsequent text analytic applications. We built the named entity normalization model with a novel Edge Weight Updating Neural Network. Our proposed model when tested on four different datasets achieved state-of-the-art results. We, next, verify our model's performance on NCBI Disease, BC5CDR Disease, and BC5CDR Chemical databases, which are widely used named entity normalization datasets in the bioinformatics field. We also tested our model with our own financial named entity normalization dataset to validate the efficacy for more general applications. Using the constructed dataset, we differentiate named entity pairs. Our model achieved the highest named entity normalization performances in terms of various evaluation metrics.