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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Enhancing biomedical named entity recognition with parallel boundary detection and category classification.

Yu Wang¹, Hanghang Tong², Ziye Zhu³

¹School of Science, China Pharmaceutical University, Nanjing, China. wangyu@cpu.edu.cn.

BMC Bioinformatics

|February 25, 2025

Summary

This summary is machine-generated.

We introduce BEAN, a novel parallel model for biomedical named entity recognition (BioNER). BEAN effectively handles nested structures and category correlations, achieving state-of-the-art results on multiple datasets.

Keywords:

Biomedical domain Biomedical named entity recognition Named entity recognition Natural language processing Text mining

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Area of Science:

Natural Language Processing
Bioinformatics
Computational Biology

Background:

Biomedical Named Entity Recognition (BioNER) is crucial for advanced applications.
BioNER faces challenges due to nested structures and category correlations in entities.
Existing models struggle to balance nested structure handling and category knowledge integration.

Purpose of the Study:

To present a novel parallel BioNER model, BEAN.
To address the unique properties of biomedical entities.
To balance nested structures and category correlations in BioNER.

Main Methods:

Developed a parallel BioNER model named BEAN.
Utilized a triaffine model exploiting head, tail, and contextualized features for boundary detection.
Introduced a multi-label classification model for category extraction without boundary guidance.

Main Results:

BEAN achieves state-of-the-art performance on five public NER datasets, including four biomedical datasets.
Demonstrated effectiveness in handling nested structures and category correlations.
Showcased balanced performance between entity boundary detection and category classification.

Conclusions:

BEAN is the first BioNER model to handle nested structures and category correlations in parallel.
The model effectively detects entity boundaries and classifies categories.
BEAN offers an efficient approach to BioNER, advancing the field.