Nested Dolls: Towards Unsupervised Clustering of Web Tables

Here we discuss our initial efforts towards unsupervised clustering of a large-scale Web tables dataset. We improve our previous approach of weakly-supervised clustering, where an operator would provide a few descriptive keywords to generate an entity-identifying classifier, which is applied to the corpora to form a cohesive entity-centric cluster [1]. Here, we make a next step towards fully unsupervised algorithm by automatically generating these descriptive keywords. These keywords then can be used to generate high-precision training data and train a classifier to form a cluster. Here, we describe and evaluate this new unsupervised keyword generation algorithm and apply it to a large-scale Web tables corpus to form initial small high-precision clusters.