Multi-Label Approaches to Web Genre Identification

Abstract

A web page is a complex document which can share conventions of several genres, or contain several parts, each belonging to a different genre. To properly address the genre interplay, a recent proposal in automatic web genre identification is multi-label classification. The dominant approach to such classification is to transform one multi-label machine learning problem into several sub-problems of learning binary single-label classifiers, one for each genre. In this paper we explore multi-class transformation, where each combination of genres is labeled with a single distinct label. This approach is then compared to the binary approach to determine which one better captures the multi-label aspect of web genres. Experimental results show that both of the approaches failed to properly address multi-genre web pages. Obtained differences were a result of the variations in the recognition of one-genre web pages.