BorderFlow: A Local Graph Clustering Algorithm for Natural Language Processing

In this paper, we introduce BorderFlow, a novel local graph clustering algorithm, and its application to natural language processing problems. For this purpose, we first present a formal description of the algorithm. Then, we use BorderFlow to cluster large graphs and to extract concepts from word similarity graphs. The clustering of large graphs is carried out on graphs extracted from the Wikipedia Category Graph. The subsequent low-bias extraction of concepts is carried out on two data sets consisting of noisy and clean data. We show that BorderFlow efficiently computes clusters of high quality and purity. Therefore, BorderFlow can be integrated in several other natural language processing applications.

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