从自然语言文本中挖掘概念

V. Rockai, M. Mach
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引用次数: 2

摘要

WordNet词典是自然语言处理领域中常用的资源。许多论文讨论了WordNet词典的主题,但致力于其自动化构建的过程仍有很大的改进空间。其中许多是基于机器翻译策略,将字典从一种语言转换为另一种语言。它们中的大多数都有一个共同的属性:它们以字典甚至简单语法分析器的形式使用有关所使用语言的知识。在WordNet词典中,术语以概念层次结构的形式表示(上、下)。由于ALOC方法被用于实现类似的概念结构,我们可以假设它也可以用于自动WordNet字典构建领域。本文讨论了ALOC的这种应用,其形式是将新概念正确分配到现有的WordNet层次结构中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Concept mining from natural language texts
WordNet dictionaries are a commonly used resource in the NLP field. Many papers discuss the theme of WordNet dictionaries, but the processes devoted to their automated construction have still much space for improvement. Many of them are based on machine translation strategies converting the dictionaries from one language to another. Most of them have one attribute in common: they use the knowledge about the language used in the form of dictionaries or even simple grammar parsers. In WordNet dictionaries, terms are represented in the form of concept hierarchies (hypernyms, hyponyms, ...). Since the ALOC approach was used to achieve similar concept structures, we can assume that it could also be used in the area of automated WordNet dictionary construction. This paper discusses such application of ALOC, in the form of correct assignment of new concepts into the existing WordNet hierarchy.
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