Phrase Ranking and Wikipedia Based Cluster Labeling

Automatically labeling document clusters with words which indicate their topics is a relatively new and active research field. The most frequently used process, labeling with the most frequent words in the clusters, turns out using several words that are virtually void of descriptive power even after traditional stop words are eliminated. Another procedure, labeling with the most anticipated words, often include rather obscure results. We present Phrase Rank, a variation of the Page Rank algorithm based on relational graph representation of the content of web document collections. Phrase Rank achieves segregation and ranking of discriminative phrases higher than the ambiguous Phrases followed by common phrases. Thus a set of important text features are first extracted from the cluster documents. Further we use these features to extract cluster labels from the external knowledge sources such as pre-categorized knowledge of Wikipedia. We experiment with a test dataset to demonstrate the efficacy of Phrase Rank algorithm.