ICS 278 Data MiningLecture 15 Text Classification.ppt

ICS 278: Data MiningLecture 15: Text Classification Padhraic Smyth Department of Information and Computer Science University of California, Irvine RoadMap for Lectures Lecture 15 (today): text classification Lectures 16, 17, 18, 19: Unsupervised learning from text – clustering and topic modeling Recommender systems Credit scoring applications Pattern-finding algorithms Lecture 20 Thursday June 8th (2 weeks from Thursday) 5-minute project summary from each student More details on format to come later….. Text Classification Text classification has many applications Spam email detection Automated tagging of streams of news articles, e.g., Google News Automated creation of Webtaxonomies Data Representation “Bag of words” most commonly used: either counts or binary Can also use “phrases” for commonly occuring combinations of words Classification Methods Na?ve Bayes widely used (e.g., for spam email) Fast and reasonably accurate Support vector machines (SVMs) Typically the most accurate method in research studies But more complex computationally Logistic Regression (regularized) Not as widely used, but can be competitive with SVMs (e.g., Zhang and Oles, 2002) Further Reading on Text Classification Web-related text mining in general S. Chakrabarti, Mining the Web: Discovering Knowledge from Hypertext Data, Morgan Kaufmann, 2003. See chapter 5 for discussion of text classification General references on text and language modeling Foundations of Statistical Language Processing, C. Manning and H. Schutze, MIT Press, 1999. Speech and Language Processing: An Introduction to Natural Language Processing, Dan Jurafsky and James Martin, Prentice Hall, 2000. SVMs for text classification T. Joachims, Learning to Classify Text using Support Vector Machines: Methods, Theory and Algorithms, Kluwer, 2002 Common Data Sets used for Evaluation Reuters 10700 labeled documents 10% documents with multiple class labels Yahoo! Science Hierarchy 95 disjoint classes with 13,598 pa