An Adaptive Approach to Spam Filtering on a New Corpus
نویسنده
چکیده
Motivated by the absence of rigorous experimentation in the area of spam filtering using realistic email data, we present a newly-assembled corpus of genuine and unsolicited (spam) email, dubbed GenSpam, to be made publicly available. We also propose an adaptive model for semi-structured document classification based on smoothed n-gram language modelling and interpolation, and report promising results when applying the classifier to the spam filtering problem using a specifically assembled test set to be released as part of the GenSpam corpus.
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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 mod...
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