Fast text categorization using concise semantic analysis
Text representation is a necessary procedure for text categorization tasks. Currently, bag of words (BOW) is the most widely used text representation method but it suffers from two drawbacks. First, the quantity of words is huge; second, it is not feasible to calculate the relationship between words. Semantic analysis (SA) techniques help BOW overcome these two drawbacks by interpreting words and documents in a space of concepts. However, existing SA techniques are not designed for text categorization and often incur huge computing cost. This paper proposes a concise semantic analysis (CSA) technique for text categorization tasks. CSA extracts a few concepts from category labels and then implements concise interpretation on words and documents. These concepts are small in quantity and great in generality and tightly related to the category labels. Therefore, CSA preserves necessary information for classifiers with very low computing cost. To evaluate CSA, experiments on three data sets (Reuters-21578, 20-NewsGroup and Tancorp) were conducted and the results show that CSA reaches a comparable micro- and macro-F1 performance with BOW, if not better one. Experiments also show that CSA helps dimension sensitive learning algorithms such as k-nearest neighbor (kNN) to eliminate the Curse of Dimensionality and as a result reaches a comparable performance with support vector machine (SVM) in text categorization applications. In addition, CSA is language independent and performs equally well both in Chinese and English.
text categorization semantic analysis text representation dimensionality reduction