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Related Experiment Video

Updated: May 16, 2026

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Improving protein coreference resolution by simple semantic classification.

Ngan Nguyen¹, Jin-Dong Kim, Makoto Miwa

¹National Institute of Informatics, Hitotsubashi 2-1-2, Chiyoda-ku, Tokyo, Japan. ngan@nii.ac.jp

BMC Bioinformatics

|November 20, 2012

Summary

This summary is machine-generated.

Domain-specific information significantly improves protein coreference resolution in biomedical literature. Incorporating protein type data into semantic classification enhanced system performance, outperforming existing methods.

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Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

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An Integrated Approach for Microprotein Identification and Sequence Analysis

An Integrated Approach for Microprotein Identification and Sequence Analysis

Published on: July 12, 2022

Area of Science:

Biomedical Natural Language Processing
Computational Biology
Information Extraction

Background:

Coreference resolution is crucial for biomedical event extraction, but challenges arise from domain-specific phenomena.
The Protein Coreference (COREF) task in BioNLP-ST 2011 highlighted difficulties in applying general coreference methods to molecular biology literature.

Purpose of the Study:

To investigate the impact of domain-specific information, particularly protein type, on rule-based protein coreference resolution.
To develop and evaluate a system that effectively utilizes biological domain knowledge for improved coreference resolution.

Main Methods:

A rule-based protein coreference resolution system was developed.
Domain-specific information, including protein type, was encoded into semantic classification modules.
The system's performance was evaluated against top systems from the BioNLP-ST 2011.

Main Results:

The proposed system, even in its minimal configuration, outperformed the best system from BioNLP-ST 2011.
Semantic classification using protein information led to performance improvements of 2.3% (test data) and 4.0% (development data) in F-score.

Conclusions:

Domain-specific information is vital for effective coreference resolution in the biomedical domain.
Developing methods to leverage this information is essential due to the difficulty of cross-domain transfer.