Using Compression For Source Based Classification Of

This thesis addresses the problem of source based text classification. In a nutshell, this problem involves classifying documents according to "where they came from" instead of the usual "what they contain". Viewed from a machine learning perspective, this can be looked upon as a learning problem and can be classified into two categories: supervised and unsupervised learning. In the former case, the classifier is presented with known examples of documents and their sources during the training phase. In the testing phase, the classifier is given a document whose source is unknown, and the goal of the classifier is to find the most likely one from the category of known sources. In the latter case, the classifier is just presented with samples of text, and its goal is to detect regularities in the data set. One such goal could be a clustering of the documents based on common authorship. In order to perform these classification tasks, we intend to use compression as the underlying technique. Compression can be viewed as a predict-encode process where the prediction of upcoming tokens is done by adaptively building a model from the text seen so far. This source modelling feature of compression algorithms allows for classification by purely statistical means. Thesis Supervisor: Shafi Goldwasser Title: RSA Professor of Computer Science and Engineering