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Related Experiment Video

Updated: May 10, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Wide coverage biomedical event extraction using multiple partially overlapping corpora.

Makoto Miwa1, Sampo Pyysalo, Tomoko Ohta

  • 1The National Centre for Text Mining and School of Computer Science, Manchester Institute of Biotechnology, University of Manchester, 131 Princess Street, Manchester, M1 7DN, UK. makoto.miwa@manchester.ac.uk

BMC Bioinformatics
|June 5, 2013
PubMed
Summary
This summary is machine-generated.

Learning from multiple biomedical corpora with overlapping event types creates a unified, wide-coverage extraction system. This approach enhances event extraction accuracy and efficiency, outperforming single-corpus models.

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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

Area of Science:

  • Biomedical informatics
  • Natural Language Processing
  • BioNLP

Background:

  • Biomedical event extraction is crucial for analyzing physiological processes and diseases.
  • Manual annotation of corpora is time-consuming and limits semantic type coverage.
  • Limited research exists on leveraging multiple corpora with overlapping annotations for event extraction.

Purpose of the Study:

  • To develop a method for training event extraction systems using multiple corpora with partially overlapping semantic annotations.
  • To improve the EventMine system's wide-coverage event extraction capabilities.
  • To enable the creation of a single, corpus-independent event extraction model.

Main Methods:

  • Proposed a novel method for multi-corpus learning with partial semantic annotation overlap.
  • Implemented the method to enhance the existing EventMine event extraction system.
  • Evaluated the system using seven event-annotated corpora, covering 65 event types.

Main Results:

  • The developed method enables learning from overlapping corpora to create a single, wide-coverage extraction system.
  • The improved EventMine system outperforms systems trained on single corpora.
  • Achieved new state-of-the-art results on two BioNLP Shared Task 2011 event extraction tasks.

Conclusions:

  • The proposed method facilitates training a state-of-the-art, wide-coverage event extraction system from multiple, partially overlapping corpora.
  • A single model simplifies broad-coverage extraction, eliminating the need for corpus selection or result merging.
  • Enables more efficient annotation efforts focused on expanding semantic type coverage.