We present a detailed theoretical and computational analysis of the Watset meta-algorithm for fuzzy graph clustering, which has been found to be widely applicable in a variety of domains. This algorithm creates an intermediate representation of the input graph that reflects the “ambiguity” of its nodes. Then, it uses hard clustering to discover clusters in this “disambiguated” intermediate graph. After outlining the approach and analyzing its computational complexity, we demonstrate that Watset shows competitive results in three applications: unsupervised synset induction from a synonymy graph, unsupervised semantic frame induction from dependency triples, and unsupervised semantic class induction from a distributional thesaurus. Our algorithm is generic and can be also applied to other networks of linguistic data.

中文翻译：

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Watset：局部全局图聚类，在感知和帧归纳中的应用

我们对用于模糊图聚类的 Watset 元算法进行了详细的理论和计算分析，该算法已被广泛应用于各种领域。该算法创建输入图的中间表示，反映其节点的“歧义”。然后，它使用硬聚类在这个“消除歧义”的中间图中发现聚类。在概述了该方法并分析了其计算复杂性之后，我们证明了 Watset 在三个应用程序中显示出有竞争力的结果：来自同义词图的无监督同义词集归纳、来自依赖三元组的无监督语义框架归纳和来自分布式同义词表的无监督语义类归纳。我们的算法是通用的，也可以应用于其他语言数据网络。