TAILIEUCHUNG - Báo cáo khoa học: "A Comparison of Event Models for Naive Bayes Anti-Spam E-Mail Filtering"

We describe experiments with a Naive Bayes text classifier in the context of anti- spam E-mail filtering, using two different statistical event models: a multi-variate Bernoulli model and a multinomial model. We introduce a family of feature ranking functions for feature selection in the multinomial event model that take account of the word frequency information. We present evaluation results on two publicly available corpora of legitimate and spam E-mails. | A Comparison of Event Models for Naive Bayes Anti-Spam E-Mail Filtering Karl-Michael Schneider University of Passau Department of General Linguistics Innstr. 40 D-94032 Passau schneide@ Abstract We describe experiments with a Naive Bayes text classifier in the context of anti-spam E-mail filtering using two different statistical event models a multi-variate Bernoulli model and a multinomial model. We introduce a family of feature ranking functions for feature selection in the multinomial event model that take account of the word frequency information. We present evaluation results on two publicly available corpora of legitimate and spam E-mails. We find that the multinomial model is less biased towards one class and achieves slightly higher accuracy than the multi-variate Bernoulli model. 1 Introduction Text categorization is the task of assigning a text document to one of several predefined categories. Text categorization plays an important role in natural language processing NLP and information retrieval 1R applications. One particular application of text categorization is anti-spam E-mail filtering where the goal is to block unsolicited messages with commercial or pornographic content UCE spam from a user s E-mail stream while letting other legitimate messages pass. Here the task is to assign a message to one of two categories legitimate and spam based on the message s content. In recent years a growing body of research has applied machine learning techniques to text categorization and anti-spam E-mail filtering including rule learning Cohen 1996 Naive Bayes Sahami et al. 1998 Androutsopoulos et al. 2000b Rennie 2000 memory based learning Androutsopoulos et aL 2000b decision trees Carreras and Marquez 2001 support vector machines Drucker et al. 1999 or combinations of different learners Sakkis et al. 2001 . In these approaches a classifier is learned from training data rather than constructed by hand which results in better and more robust .

• Báo cáo khoa học
• A Comparison of Event Models
• Naive Bayes Anti Spam
• E Mail Filtering
• scientific reports
• model language
• process natural language
• health impacts
• frequency fields
• Comprehensive assessment
• hazardous effects
• nanomaterials derived
• protein aggregation
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln