Keyword clustering for automatic categorization

Processing short texts is becoming a trend in information retrieval. Since the text has rarely external information, it is more challenging than document. In this paper, keyword clustering is studied for automatic categorization. To obtain semantic similarity of the keywords, a broad-coverage lexical resource WordNet is employed. We introduce a semantic hierarchical clustering. For automatic keyword categorization, a validity index for determining the number of clusters is proposed. The minimum value of the index indicates the potentially appropriate categorization. We show the result in experiments, which indicates the index is effective.