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

机译:使用域自适应和共指解析来促进从文献中自动提取事件

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摘要

Motivation: In recent years, several biomedical event extraction (EE) systems have been developed. However, the nature of the annotated training corpora, as well as the training process itself, can limit the performance levels of the trained EE systems. In particular, most event-annotated corpora do not deal adequately with coreference. This impacts on the trained systems' ability to recognize biomedical entities, thus affecting their performance in extracting events accurately. Additionally, the fact that most EE systems are trained on a single annotated corpus further restricts their coverage.
机译:动机:近年来,已经开发了几种生物医学事件提取(EE)系统。但是,带注释的训练语料库的性质以及训练过程本身会限制训练后的EE系统的性能水平。特别是,大多数带事件注释的语料库不能充分处理共指。这会影响受过训练的系统识别生物医学实体的能力,从而影响其准确提取事件的性能。此外,大多数EE系统都是在单个带注释的语料库上进行训练的事实进一步限制了它们的覆盖范围。

著录项

  • 来源
    《Bioinformatics》 |2012年第13期|p.1759-1765|共7页
  • 作者

    Sophia Ananiadou;

  • 作者单位
  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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