自动提取同义词信息的质量评价

A. Kumaran, R. Makin, Vijay Pattisapu, Shaik Sharif, Lucy Vanderwende
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引用次数: 0

摘要

语义信息的自动提取如果成功,将为资源少或资源差的语言提供低成本创建本体论资源的前景,从而为这些语言中的常识推理应用提供支持。在本文中,我们探索了使用两种互补技术从大型语料库中自动提取同义词信息:一种通用的广泛覆盖的语法分析器,用于生成语义信息位,以及使用自动语义消歧将其合成为同义词集。为了验证这样提取的同义词信息的质量,我们以英语为实验对象,因为已经有适当的语义资源可用。我们从一个大型语料库中挑选同义词信息,并将其与几个标准来源中的同义词信息进行比较。我们提出了我们的方法的结果,无论是定量的还是定性的,都表明使用所提出的方法可以从大型语料库中自动提取高质量的同义词信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluating the Quality of Automatically Extracted Synonymy Information
Automatic extraction of semantic information, if successful, offers to languages with little or poor resources, the prospects of creating ontological resources inexpensively, thus providing support for common-sense reasoning applications in those languages. In this paper we explore the automatic extraction of synonymy information from large corpora using two complementary techniques: a generic broad-coverage parser for generation of bits of semantic information, and their synthesis into sets of synonyms using automatic sense-disambiguation. To validate the quality of the synonymy information thus extracted, we experiment with English, where appropriate semantic resources are already available. We cull synonymy information from a large corpus and compare it against synonymy information available in several standard sources. We present the results of our methodology, both quantitatively and qualitatively, that indicate good quality synonymy information may be extracted automatically from large corpora using the proposed methodology.
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