The Impact of Noise in Web Genre Identification

Genre detection of web documents fits an open-set classification task. The web documents not belonging to any predefined genre or where multiple genres co-exist is considered as noise. In this work we study the impact of noise on automated genre identification within an open-set classification framework. We examine alternative classification models and document representation schemes based on two corpora, one without noise and one with noise showing that the recently proposed RFSE model can remain robust with noise. Moreover, we show how that the identification of certain genres is not practically affected by the presence of noise.