Text Classification with Compression Algorithms
This addresses text categorization challenges by offering a language-independent method that avoids preprocessing, though it is incremental with computational limitations.
The paper tackles text classification by proposing a kernel function based on compressed lengths to estimate similarity between text objects, achieving greater accuracy than Gaussian, linear, and polynomial kernels on datasets like Web-KB, 20ng, and Reuters-21578.
This work concerns a comparison of SVM kernel methods in text categorization tasks. In particular I define a kernel function that estimates the similarity between two objects computing by their compressed lengths. In fact, compression algorithms can detect arbitrarily long dependencies within the text strings. Data text vectorization looses information in feature extractions and is highly sensitive by textual language. Furthermore, these methods are language independent and require no text preprocessing. Moreover, the accuracy computed on the datasets (Web-KB, 20ng and Reuters-21578), in some case, is greater than Gaussian, linear and polynomial kernels. The method limits are represented by computational time complexity of the Gram matrix and by very poor performance on non-textual datasets.