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Udo Hahn¹, Elena Beisswanger, Ekaterina Buyko

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Summary

This summary is machine-generated.

This study introduces PathoJen, a new corpus for annotating pathological phenomena in medical texts. It uses a novel two-category system and active learning to improve disease entity recognition efficiently.

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Area of Science:

Natural Language Processing
Medical Informatics
Computational Linguistics

Background:

Accurate identification of pathological phenomena in biomedical literature is crucial for medical research and clinical applications.
Existing annotation guidelines struggle to capture the nuances of disease mentions, especially less structured descriptive statements.
Developing robust named entity recognition (NER) systems for pathology requires high-quality annotated data.

Purpose of the Study:

To present the design decisions and annotation procedures for PathoJen, a novel corpus of Medline abstracts annotated for pathological phenomena.
To introduce a two-category encoding schema to differentiate between standardized disease terminology and descriptive pathological statements.
To implement an Active Learning-based sampling strategy to optimize annotation cost and quality.

Main Methods:

Development of PathoJen corpus using Medline abstracts.
Implementation of a two-category encoding schema: short mentions for standardized terms (e.g., diseases) and long mentions for descriptive pathological statements.
Application of an Active Learning approach for intelligent sampling of annotation instances to reduce costs and improve model performance.

Main Results:

PathoJen corpus successfully annotated for pathological phenomena, including diseases.
The two-category encoding schema effectively distinguishes between different types of pathological mentions.
Active Learning strategy demonstrated efficiency in annotation by focusing on challenging instances, maintaining quality.

Conclusions:

PathoJen provides a valuable resource for training and evaluating NER systems for pathological entities.
The proposed annotation schema and Active Learning approach offer a cost-effective method for building specialized biomedical corpora.
This work advances the field of biomedical NLP by improving the capture of complex pathological information.