Hierarchical Verb Clustering Using Graph Factorization
Hierarchical Verb Clustering Using Graph Factorization
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发表时间：
2011-07
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通讯作者：
Lin Sun;A. Korhonen
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作者：
Lin Sun;A. Korhonen
以往的动词聚类研究主要集中在从语料库数据中获取扁平的分类，尽管许多手动构建的分类本质上是分类的。自然语言处理（nlp）应用程序也受益于分类学分类，因为它们在分类所需的粒度方面有所不同。我们引入了一种新的聚类方法称为层次图分解聚类（hgfc），并扩展它，使它是最佳的任务。我们的研究结果表明，Hgfc优于经常使用的凝聚聚类从VerbNet中提取的分层测试集，它产生的最先进的性能也在一个平坦的测试集。我们演示了如何使用该方法来获得新的分类以及扩展现有的一些先验知识的基础上的分类。
Most previous research on verb clustering has focussed on acquiring flat classifications from corpus data, although many manually built classifications are taxonomic in nature. Also Natural Language Processing (nlp) applications benefit from taxonomic classifications because they vary in terms of the granularity they require from a classification. We introduce a new clustering method called Hierarchical Graph Factorization Clustering (hgfc) and extend it so that it is optimal for the task. Our results show that Hgfc outperforms the frequently used agglomerative clustering on a hierarchical test set extracted from VerbNet, and that it yields state-of-the-art performance also on a flat test set. We demonstrate how the method can be used to acquire novel classifications as well as to extend existing ones on the basis of some prior knowledge about the classification.