PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Using string kernels for classification of Slovenian Web documents
Blaz Fortuna and Dunja Mladenić
In: 29th Annual Conference of the German Classification Society, 9-11 March 2005, Magdeburg, Germany.


In this paper we present an approach for classifying web pages obtained from the Slovenian Internet directory where the web sites covering different topics are organized into a topic ontology.We tested two different methods for representing text documents, both in combination with the linear SVM classification algorithm. The first representation that we have used is a standard bag-of-words approach with TFIDF weights and cosine distance used as similarity measure. We compared this to String kernels where text documents are compared not by words but by substrings. This removes the need for stemming or lemmatisation which can be important issue when documents are in languages other than English and tools for stemming or lemmatisation are unavailable or are expensive to make or learn. In highly inflected natural languages, such as Slovene language, the same word can have many different forms, thus String kernels have an advantage here over the bagof- words. In this paper we show that on classification of documents written in highly inflected natural language the situation is opposite and String Kernels significantly outperform the standard bag-of-words representation. Our experiments also show that the advantage of String kernels is more evident for domains with unbalanced class distribution.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:1207
Deposited By:Blaz Fortuna
Deposited On:24 November 2005