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Using Latent Semantic Indexing to Filter Spam (2003)
| Venue: | In Proceedings of the 2003 ACM symposium on Applied computing |
| Citations: | 34 - 0 self |
BibTeX
@INPROCEEDINGS{Gee03usinglatent,
author = {Kevin R. Gee},
title = {Using Latent Semantic Indexing to Filter Spam},
booktitle = {In Proceedings of the 2003 ACM symposium on Applied computing},
year = {2003},
pages = {460--464},
publisher = {ACM Press}
}
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Abstract
Past research has explored the effectiveness of a Naive Bayesian classifier when filtering unsolicited bulk email (or "spare"). Results have shown that The degree of precision of this approach is generally superior to The degree of recall. This study evaluates The effectiveness of a classifier incorporating LaTent Semantic Indexing ("LSP') to filter spare email, using a corpus used in previous studies. Results show that using LSI as The basis for an email classifier to filter out spare enjoys a very high degree of recall as well as a high degree of precision, no matter if The corpus is treated using a stop list or a lemmadzer. While using LSI leads to precision roughly equal to That of using a Naive Bayesian approach, the LSI technique has a substantially highes' recall and is generally more effective under certain conditions.
