Resolution of Coordination Ellipses in Biological Named Entities Using Conditional Random Fields
Resolution of Coordination Ellipses in Biological Named Entities Using Conditional Random Fields
复制标题
使用条件随机场解析生物命名实体中的协调椭圆
DOI：
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发表时间：
2007
期刊：
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通讯作者：
E. Buyko
中科院分区：
文献类型：
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作者：
E. Buyko
协调省略者是一种语言现象，其中从协调的语言表面表达中消除了相关信息。因此，假设读者可以在适当的背景知识的情况下轻松地重建消除的材料。这种现象在科学术语中特别丰富。就指定的实体识别（NER）而言，无法解决省略号的解决方案导致NER系统性能的严重退化。通常，他们错误地识别椭圆协调，因为复杂的单个实体提到或仅正确地对协调的非胸腔分析进行了分类。作为替代方案，我们提出了一种将复杂协调的实体表达式分解为组成符合物的方法，用于确定缺失的元素，从而明确重建所有单个实体提及。我们在这里提出了一种基于机器学习的新型方法，用于解决名词短语中椭圆协调的方法。对于结合识别的任务，模型在生物医学G ENIA语料库上的性能达到了93％。
Coordination ellipsis is a linguistic phenomenon where relevant information is eliminated from the linguistic surface expression of a coordination. Thereby, the assumption is that readers can easily reconstruct the eliminated material given appropriate background knowledge. This phenomenon is particularly abundant in science jargon. As far as named entity recognition (NER) is concerned, failing resolution of coordination ellipsis leads to serious degradation in the performance of NER systems. Usually, they recognize elliptical coordinations, wrongly, as complex single entity mentions or classify correctly only non-elliptical parts of coordinations. As an alternative, we propose a methodology for decomposing complex coordinated entity expressions into constituent conjuncts, for determining the missing elements and thus reconstructing explicitly all of the single entity mentions. We present here a novel supervised machine learning-based approach to the resolution of elliptical coordinations in noun phrases. For the task of conjunct identification the model achieves performance of 93% on the biomedical G ENIA corpus.
语料库及其注释。
DOI：
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发表时间：
2006
期刊：
影响因子：
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作者：
Kim;Jin-Dong;Jun'icni Tsujii.
通讯作者：
Jun'icni Tsujii.