What exacerbates these problems in text processing approaches is that texts are typically represented with features that are at the wrong level of granularity for what is being modeled.
for practical reasons, the most common types of features used in text classification tasks are still unigrams, bigrams, and part-of-speech bigrams, which are highly prone to over-fitting.