Classifying Taxonomic Relations between Pairs of Wikipedia Articles
Classifying Taxonomic Relations between Pairs of Wikipedia Articles
复制标题
对维基百科文章对之间的分类关系进行分类
DOI：
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
K. McKeown
中科院分区：
文献类型：
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作者：
Or Biran;K. McKeown
自然语言生成系统依赖于分类主题词表来执行词汇选择和聚合等任务。Wordnet就是这样一个分类法，但它的大小是有限的。出于科学文献领域生成系统的需要，我们提出了一种从维基百科文章构建分类词库的方法，其中每一篇文章代表分类中的一个潜在概念。我们提出将建立分类的问题框定为对单个维基百科文章对之间的潜在关系进行分类的任务，并证明了有监督的算法可以在很少的训练数据的情况下达到很高的精度。
Natural language generation systems rely on taxonomic thesauri for tasks such as lexical choice and aggregation. WordNet is one such taxonomy, but it is limited in size. Motivated by the needs of a generation system in the scientific literature domain, we present a method for building a taxonomic thesaurus from Wikipedia articles, where each article represents a potential concept in the taxonomy. We propose framing the problem of creating a taxonomy as a classification task of the potential relations between individual Wikipedia article pairs, and show that a supervised algorithm can achieve high precision in this task with very little training data.