Overview of the Cancer Genetics (CG) task of BioNLP Shared Task 2013
Overview of the Cancer Genetics (CG) task of BioNLP Shared Task 2013
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BioNLP 共享任务 2013 癌症遗传学 (CG) 任务概述
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
S. Ananiadou
中科院分区：
文献类型：
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作者：
Sampo Pyysalo;Tomoko Ohta;S. Ananiadou
我们介绍了癌症遗传学（CG）事件提取任务的设计，准备，结果和分析，这是BioNLP共享任务（ST）2013的主要任务。CG任务是一种信息提取任务，目标是识别文本中的事件，表示为给定物理实体的结构化n元关联。除了解决癌症领域外，CG任务与BioNLP ST系列中以前的事件提取任务不同，它解决了广泛的病理过程和多层次的生物组织，从分子到细胞和器官水平直至整个生物体。最终的测试集提交接受了六个团队。性能最高的系统获得了55.4%的F分数。这种性能水平与现有分子水平提取任务的最新技术水平大致相当，表明事件提取资源和方法可以很好地推广到更高水平的生物组织，并适用于癌症科学文本的分析。CG任务仍然是对所有相关方的开放式挑战，工具和资源可从http：//2013获得。bionlp-st.org/.
We present the design, preparation, results and analysis of the Cancer Genetics (CG) event extraction task, a main task of the BioNLP Shared Task (ST) 2013. The CG task is an information extraction task targeting the recognition of events in text, represented as structured n-ary associations of given physical entities. In addition to addressing the cancer domain, the CG task is differentiated from previous event extraction tasks in the BioNLP ST series in addressing a wide range of pathological processes and multiple levels of biological organization, ranging from the molecular through the cellular and organ levels up to whole organisms. Final test set submissions were accepted from six teams. The highest-performing system achieved an Fscore of 55.4%. This level of performance is broadly comparable with the state of the art for established molecular-level extraction tasks, demonstrating that event extraction resources and methods generalize well to higher levels of biological organization and are applicable to the analysis of scientific texts on cancer. The CG task continues as an open challenge to all interested parties, with tools and resources available from http://2013. bionlp-st.org/.
DOI：
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发表时间：
2013-08
期刊：
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影响因子：
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作者：
Makoto Miwa;S. Ananiadou
通讯作者：
Makoto Miwa;S. Ananiadou