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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts
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Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts

机译:提取与生物医学文本事件将生物学关系相关联的刑期关系

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摘要

We present an analysis of the problem of identifying biological context and associating it with biochemical events described in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type, and cell type that are associated with biochemical events. We present a new corpus of open access biomedical texts that have been annotated by biology subject matter experts to highlight context-event relations. Using this corpus, we evaluate several classifiers for context-event association along with a detailed analysis of the impact of a variety of linguistic features on classifier performance. We find that gradient tree boosting performs by far the best, achieving an F1 of 0.865 in a cross-validation study.
机译:我们对鉴定生物学背景和与生物医学文本中描述的生物化学事件相关联的问题分析。这构成了非琐碎的间互连关系的提取任务。我们将重点关注生物学背景作为与生物化学事件相关的物种,组织类型和细胞类型的描述。我们展示了一项新的开放式生物医学文本语料库,已被生物学主题专家注释,以突出上下文关系。使用此语料库,我们为上下文事件协会评估了多个分类器,并详细分析了各种语言特征对分类器性能的影响。我们发现渐变树提升到遥远的速度,在交叉验证研究中实现了0.865的F1。

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