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Related Experiment Video

Updated: May 22, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Boosting automatic event extraction from the literature using domain adaptation and coreference resolution.

Makoto Miwa¹, Paul Thompson, Sophia Ananiadou

¹The National Centre for Text Mining (NaCTeM), UK. makoto.miwa@manchester.ac.uk

Bioinformatics (Oxford, England)

|April 28, 2012

Summary

This study enhances biomedical event extraction (EE) by integrating coreference resolution (CR) and domain adaptation (DA) into the EventMine system. These improvements significantly boost EE performance and coverage, outperforming previous top systems.

Related Experiment Videos

Last Updated: May 22, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Biomedical Natural Language Processing
Information Extraction

Background:

Existing biomedical event extraction (EE) systems face performance limitations due to inadequate handling of coreference in training corpora.
Training EE systems on single corpora restricts their overall coverage and accuracy in recognizing biomedical entities and events.

Purpose of the Study:

To enhance the EventMine system for improved biomedical event extraction.
To address limitations in coreference and corpus coverage in EE systems.

Main Methods:

Integrated a novel coreference resolution (CR) system, achieving high performance in resolving anaphoric references to proteins.
Incorporated domain adaptation (DA) methods to enable training on multiple annotated corpora, expanding EE coverage.
Developed methods to increase the generality and efficiency of the EventMine system.

Main Results:

The integrated CR system surpasses top-ranked systems in the BioNLP'11 COREF subtask.
The enhanced EventMine system demonstrates significant improvements in EE F-Score (0.5%-3.4%).
The improved EventMine outperforms leading systems from BioNLP'09 and BioNLP'11 shared tasks.

Conclusions:

The combination of CR and DA significantly advances biomedical event extraction capabilities.
The enhanced EventMine system offers improved accuracy and broader coverage for biomedical information extraction.
The updated EventMine system is publicly available for research use.