An Adaptive, Semi-Structured Language Model Approach to Spam Filtering on a New Corpus

Motivated by current efforts to construct more realistic spam filtering experimental corpora, we present a newly assembled, publicly available corpus of genuine and unsolicited (spam) email, dubbed GenSpam. We also propose an adaptive model for semi-structured document classification based on language model component interpolation. We compare this with a number of alternative classification models, and report promising results on the spam filtering task using a specifically assembled test set to be released as part of the GenSpam corpus.