Multinomial distributions are often used to model text documents. However, they do not capture well the phenomenon that words in a document tend to appear in bursts: if a word appears once, it is more likely to appear again. In this paper, we propose the Dirichlet compound multinomial model (DCM) as an alternative to the multinomial. The DCM model has one additional degree of freedom, which allows it to capture burstiness. We show experimentally that the DCM is substantially better than the multinomial at modeling text data, measured by perplexity. We also show using three standard document collections that the DCM leads to better classification than the multinomial model. DCM performance is comparable to that obtained with multiple heuristic changes to the multinomial model. 1.

Language independent 'bag-of-words' representations are surprisingly effective for text classification. The representation is high dimensional though, containing many non-consistent words for text categorization. These non-consistent words result in reduced generalization performance of subsequent classifiers, e.g., from ill-posed principal component transformations. In this communication, our aim is to study the effect of reducing the least relevant words from the bag-of-words representation. We consider a new approach, using neural network based sensitivity maps and information gain for determination of term relevancy, when pruning the vocabularies. With reduced vocabularies, documents are classified using a latent semantic indexing representation and a probabilistic neural network classifier. Reducing the bag-of-words vocabularies with 90%-98%, we find consistent classification improvement using two mid size data-sets. We also study the applicability of information gain and sensitivity maps for automated keyword generation.