This work addresses the problem of genre classification of text and speech transcripts, with the goal of handling genres not seen in training. Two frameworks employing different statistics on word/POS histograms with a PCA transform are examined: a single model for each genre and a factored representation of genre. The impact of the two frameworks on the classification of training-matched and new genres is discussed. Results show that the factored models allow for a finer-grained representation of genre and can more accurately characterize genres not seen in training.
['Sergey Feldman', 'Marius A. Marin', 'Julie Medero', 'Mari Ostendorf']
Classifying Factored Genres with Part-of-Speech Histograms