RECORD
Learning to Filter Spam E-Mail: A Comparison of a Naive Bayesian and a Memory-Based Approach
Computation and Language · Submitted 18 Sep 2000
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an effective method to construct automatically anti-spam filters with superior performance. We investigate thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks. At the same time, we compare the performance of the Naive Bayesian filter to an alternative memory-based learning approach, after introducing suitable cost-sensitive evaluation measures. Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. | contribution 1.00 | 0.91 | 0.04 | asserted 0.64 |
| s1 | The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s2 | Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. | background 0.91 | 0.06 | 0.02 | asserted 1.00 |
| s3 | The Naive Bayesian classifier has recently been suggested as an effective method to construct automatically anti-spam filters with superior performance. | background 1.00 | 0.05 | 0.06 | hedged possibility 0.63 |
| s4 | We investigate thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks. | contribution 0.97 | 0.94 | 0.05 | asserted 0.91 |
| s5 | At the same time, we compare the performance of the Naive Bayesian filter to an alternative memory-based learning approach, after introducing suitable cost-sensitive evaluation measures. | contribution 0.73 | 0.93 | 0.07 | asserted 0.72 |
| s6 | Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader. | result 1.00 | 0.90 | 0.81 | asserted 1.00 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far been based mostly on keyword patterns that are constructed by hand and perform poorly. The Naive Bayesian classifier has recently been suggested as an effective method to construct automatically anti-spam filters with superior performance. We investigate thoroughly the performance of the Naive Bayesian filter on a publicly available corpus, contributing towards standard benchmarks. At the same time, we compare the performance of the Naive Bayesian filter to an alternative memory-based learning approach, after introducing suitable cost-sensitive evaluation measures. Both methods achieve very accurate spam filtering, outperforming clearly the keyword-based filter of a widely used e-mail reader.
Human review
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.