Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations
Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations
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DOI：
10.3115/1220175.1220190
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发表时间：
2006-07
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影响因子：
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通讯作者：
Patrick Pantel;M. Pennacchiotti
中科院分区：
文献类型：
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作者：
Patrick Pantel;M. Pennacchiotti
在本文中，我们提出了Espresso，这是一种弱监督的、通用的、准确的用于获取语义关系的算法。主要贡献有：i)通过使用Web过滤不正确的实例来开发通用模式的方法；ii)模式和实例可靠性的原则性度量，使过滤算法成为可能。我们提出了一个经验比较的Espresso与各种状态的艺术系统，在不同的大小和类型的语料库，提取各种一般和具体的关系。实验结果表明，我们对通用模式的开发大大提高了系统的召回率，而对整体精度的影响很小。
In this paper, we present Espresso, a weakly-supervised, general-purpose, and accurate algorithm for harvesting semantic relations. The main contributions are: i) a method for exploiting generic patterns by filtering incorrect instances using the Web; and ii) a principled measure of pattern and instance reliability enabling the filtering algorithm. We present an empirical comparison of Espresso with various state of the art systems, on different size and genre corpora, on extracting various general and specific relations. Experimental results show that our exploitation of generic patterns substantially increases system recall with small effect on overall precision.