Document representations for classification of short web-page descriptions

Motivated by applying Text Categorization to classification of Web search results, this paper describes an extensive experimental study of the impact of bag-of- words document representations on the performance of five major classifiers - Naïve Bayes, SVM, Voted Perceptron, kNN and C4.5. The texts, representing short Web-page descriptions sorted into a large hierarchy of topics, are taken from the dmoz Open Directory Web-page ontology, and classifiers are trained to automatically determine the topics which may be relevant to a previously unseen Web-page. Different transformations of input data: stemming, normalization, logtf and idf, together with dimensionality reduction, are found to have a statistically significant improving or degrading effect on classification performance measured by classical metrics - accuracy, precision, recall, F1 and F2. The emphasis of the study is not on determining the best document representation which corresponds to each classifier, but rather on describing the effects of every individual transformation on classification, together with their mutual relationships.

Exploring knowledge bases for engineering a user interests hierarchy for social network applications

Master of ScienceDepartment of Computing and Information SciencesDoina CarageaGurdip SinghIn the recent years, social networks have become an integral part of our lives. Their outgrowth has resulted in opportunities for interesting data mining problems, such as interest or friendship recommendations. A global ontology over the interests specified by the users of a social network is essential for accurate recommendations. The focus of this work is on engineering such an interest ontology. In particular, given that the resulting ontology is meant to be used for data mining applications to social network problems, we explore only hierarchical ontologies. We propose, evaluate and compare three approaches to engineer an interest hierarchy. The proposed approaches make use of two popular knowledge bases, Wikipedia and Directory Mozilla, to extract interest definitions and/or relationships between interests. More precisely, the first approach uses Wikipedia to find interest definitions, the latent semantic analysis technique to measure the similarity between interests based on their definitions, and an agglomerative clustering algorithm to group similar interests into higher level concepts. The second approach uses the Wikipedia Category Graph to extract relationships between interests. Similarly, the third approach uses Directory Mozilla to extract relationships between interests. Our results indicate that the third approach, although the simplest, is the most effective for building an ontology over user interests. We use the ontology produced by the third approach to construct interest based features. These features are further used to learn classifiers for the friendship prediction task. The results show the usefulness of the ontology with respect to the results obtained in absence of the ontology

Simple Classification into Large Topic Ontology of Web Documents

The paper presents an approach to classifying Web documents into large topic ontology. The main emphasis is on having a simple approach appropriate for handling a large ontology and providing it with enriched data by including additional information on the Web page context obtained from the link structure of the Web. The context is generated from the in-coming and out-going links of the Web document we want to classify (the target document), meaning that for representing a document we use, not only text of the document itself, but also the text from the documents pointing to the target document, as well as the text from the documents the target document is pointing to. The idea is that providing enriched data is compensating for the simplicity of the approach while keeping it efficient and capable of handling large topic ontology