Multi-Label Approaches to Web Genre Identification
Vedrana Vidulin, Mitja Luštrek, Matjaž Gams · LDV-Forum/Journal for language technology and computational linguistics · 2009
Multi-Label Approaches to Web Genre IdentificationA 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.