  A substantial amount of research has been carried out in developing machine
learning algorithms that account for term dependence in text classification.
These algorithms offer acceptable performance in most cases but they are
associated with a substantial cost. They require significantly greater
resources to operate. This paper argues against the justification of the higher
costs of these algorithms, based on their performance in text classification
problems. In order to prove the conjecture, the performance of one of the best
dependence models is compared to several well established algorithms in text
classification. A very specific collection of datasets have been designed,
which would best reflect the disparity in the nature of text data, that are
present in real world applications. The results show that even one of the best
term dependence models, performs decent at best when compared to other
independence models. Coupled with their substantially greater requirement for
hardware resources for operation, this makes them an impractical choice for
being used in real world scenarios.
