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Knowledge-enhanced biomedical named entity recognition and normalization: application to proteins and genes.

Huiwei Zhou¹, Shixian Ning², Zhe Liu²

¹School of Computer Science and Technology, Dalian University of Technology, Chuangxinyuan Building, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China. zhouhuiwei@dlut.edu.cn.

BMC Bioinformatics

|February 1, 2020

Summary

This study introduces a novel knowledge-enhanced system for protein/gene named entity recognition (PNER) and normalization (PNEN). The system improves accuracy by integrating entity knowledge and contextualized word representations, achieving state-of-the-art performance.

Keywords:

Attention mechanism Contextual word representations Entity normalization Entity recognition Knowledge base

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Area of Science:

Biomedical Informatics
Computational Biology
Natural Language Processing

Background:

Automated biomedical named entity recognition and normalization are crucial for information management but face challenges from name variations and entity ambiguity.
Biomedical entities can have multiple names, and a single name might refer to different entities.
Existing methods struggle with the complexity of biomedical terminology and entity disambiguation.

Purpose of the Study:

To develop a novel knowledge-enhanced system for protein/gene named entity recognition (PNER) and normalization (PNEN).
To address challenges of name variations and entity ambiguity in biomedical text.
To improve the accuracy and performance of PNER and PNEN tasks.

Main Methods:

Developed a knowledge-enhanced system integrating entity name knowledge from biomedical databases.
Incorporated structural knowledge of entities encoded as identifier (ID) embeddings for normalization.
Utilized deep contextualized word representations from pre-trained language models to handle multi-sense entity information.

Main Results:

The proposed system achieved an F1-score of 0.871 for PNER.
The system achieved an F1-score of 0.445 for PNEN on the BioCreative VI Bio-ID corpus.
The knowledge-enhanced system demonstrated state-of-the-art performance for both PNER and PNEN tasks.

Conclusions:

A knowledge-enhanced system combining entity knowledge and deep contextualized word representations was proposed.
Entity knowledge was shown to be beneficial for PNER and PNEN tasks.
The integration of entity knowledge with contextualized information led to significant performance improvements.