Refereed full papers (journals, book chapters, international conferences)

1998

@inproceedings{PaL1998,
	vgclass =	{refpap},
	author =	{Patrick Pantel and Dekang Lin},
	title =	{SpamCop: A Spam Classification \& Organization Program},
	booktitle =	{AAAI Workshop on Learning for Text Categorization},
	address =	{Madison, Wisconsin},
	month =	{July},
	year =	{1998},
	url =	{http://www.cs.ualberta.ca/~ppantel/Download/Papers/aaai98.pdf},
	url1 =	{http://www.cs.ualberta.ca/~ppantel/Download/Papers/aaai98.ps},
	abstract =	{We present a simple, yet highly accurate, spam filtering
	program, called Spam�Cop, which is able to identify about 92\% of the
	spams while misclassifying only about 1.16\% of the nonspam e�mails.
	SpamCop treats an e�mail message as a multiset of words and employs a
	naive Bayes algorithm to determine whether or not a message is likely
	to be a spam. Compared with keyword�spotting rules, the probabilistic
	approach taken in SpamCop not only offers high accuracy, but also
	overcomes the brittleness suffered by the keyword spotting approach.},
}