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Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Making adjustments to event annotations for improved biological event extraction.

Seung-Cheol Baek1,2, Jong C Park3

  • 1Department of Computer Science, KAIST, 291 Daehak-ro, Daejeon, Republic of Korea. scbaek@nlp.kaist.ac.kr.

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|September 18, 2016
PubMed
Summary

This study introduces an Informed Expectation-Maximization (EM) algorithm to reduce inconsistencies in biological event trigger spans. The new algorithm improves the performance of biological event extraction systems.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Current biological event extraction relies on supervised learning from annotated corpora.
  • Ambiguity in event trigger spans (e.g., 'transcriptional activity' vs. 'transcriptional') leads to annotation inconsistencies.
  • These inconsistencies hinder the generalization capabilities of statistical learning algorithms.

Purpose of the Study:

  • To investigate if reducing event trigger span inconsistencies improves biological event extraction performance.
  • To propose and evaluate a novel algorithm addressing these annotation inconsistencies.

Main Methods:

  • Developed an Informed Expectation-Maximization (EM) algorithm incorporating posterior regularization.
  • Utilized gold-standard event trigger annotations as constraints within the EM algorithm.
  • Explored four specific constraints on event trigger annotations.

Main Results:

  • The proposed Informed EM algorithm significantly outperformed the state-of-the-art on the development corpus.
  • The algorithm achieved a narrow margin of improvement on the test corpus.
  • Statistical significance was demonstrated for the performance gains.

Conclusions:

  • The study confirms that reducing event trigger span ambiguity can enhance biological event extraction.
  • Analysis revealed diverse types of ambiguity in event annotations, even if infrequent.
  • The Informed EM algorithm offers a promising approach to more robust event extraction.