Using small random samples for the manual evaluation of statistical association measures

Stefan Evert; Brigitte Krenn

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Using small random samples for the manual evaluation of statistical association measures

机译：使用小的随机样本来手动评估统计关联度量

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In this paper, we describe the empirical evaluation of statistical association measures for the extraction of lexical collocations from text corpora. We argue that the results of an evaluation experiment cannot easily be generalized to a different setting. Consequently, such experiments have to be carried out under conditions that are as similar as possible to the intended use of the measures. Finally, we show how an evaluation strategy based on random samples can reduce the amount of manual annotation work significantly, making it possible to perform many more evaluation experiments under specific conditions.

机译：在本文中，我们描述了从文本语料库中提取词汇搭配的统计关联度量的经验评估。我们认为，评估实验的结果不能轻易地推广到其他环境。因此，必须在与措施的预期用途尽可能相似的条件下进行此类实验。最后，我们展示了基于随机样本的评估策略如何可以显着减少手动注释工作量，从而有可能在特定条件下执行更多评估实验。

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来源
《Computer speech and language》 |2005年第4期|p. 450-466|共17页
作者
Stefan Evert; Brigitte Krenn;
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作者单位

IMS, University of Stuttgart, Azenbergstr. 12, 70174 Stuttgart, Germany;

OFAI, Freyung 6/6, A-1010 Vienna, Austria;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
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Using small random samples for the manual evaluation of statistical association measures

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