text classification n.
Skip this Video
Loading SlideShow in 5 Seconds..
TEXT CLASSIFICATION PowerPoint Presentation
Download Presentation


6 Views Download Presentation
Download Presentation


- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. TEXT CLASSIFICATION CC437 (Includes some original material by Chris Manning)

  2. CATEGORIZATION / CLASSIFICATION • Given: • A description of an instance, xX, where X is the instance language or instance space. • E.g: how to represent text documents. • A fixed set of categories C ={c1, c2,…, cn} • Determine: • The category of x: c(x)C, where c(x) is a categorization function whose domain is X and whose range is C.


  4. 85% of all email!! TEXT CLASSIFICATION This concerns you as a patient. Our medical records indicate you have had a history of illness. We are now encouraging all our patients to use this highly effective and safe solution. Proven worldwide, feel free to read the many reports on our site from the BBC & ABC News. We highly recommend you try this Anti-Microbial Peptide as soon as possible since its world supply is limited. The results will show quickly. Regards,

  5. EXAMPLES OF TEXT CATEGORIZATION • LABELS=BINARY • “spam” / “not spam” • LABELS=TOPICS • “finance” / “sports” / “asia” • LABELS=OPINION • “like” / “hate” / “neutral” • LABELS=AUTHOR • “Shakespeare” / “Marlowe” / “Ben Jonson” • The Federalist papers

  6. Methods (1) • Manual classification • Used by Yahoo!, Looksmart,, ODP, Medline • very accurate when job is done by experts • consistent when the problem size and team is small • difficult and expensive to scale • Automatic document classification • Hand-coded rule-based systems • Reuters, CIA, Verity, … • Commercial systems have complex query languages (everything in IR query languages + accumulators)

  7. Methods (2) • Supervised learning of document-label assignment function: Autonomy, Kana, MSN, Verity, … • Naive Bayes (simple, common method) • k-Nearest Neighbors (simple, powerful) • Support-vector machines (new, more powerful) • … plus many other methods • No free lunch: requires hand-classified training data • But can be built (and refined) by amateurs

  8. Bayesian Methods • Learning and classification methods based on probability theory (see spelling / POS) • Bayes theorem plays a critical role • Build a generative model that approximates how data is produced • Uses prior probability of each category given no information about an item. • Categorization produces a posterior probability distribution over the possible categories given a description of an item.

  9. Bayes’ Rule

  10. Maximum a posteriori Hypothesis

  11. Maximum likelihood Hypothesis If all hypotheses are a priori equally likely,we only need to consider the P(D|h) term:

  12. Naive Bayes Classifiers Task: Classify a new instance based on a tuple of attribute values

  13. Naïve Bayes Classifier: Assumptions • P(cj) • Can be estimated from the frequency of classes in the training examples. • P(x1,x2,…,xn|cj) • Need very, very large number of training examples  Conditional Independence Assumption: Assume that the probability of observing the conjunction of attributes is equal to the product of the individual probabilities.

  14. Flu X1 X2 X3 X4 X5 runnynose sinus cough fever muscle-ache The Naïve Bayes Classifier • Conditional Independence Assumption: features are independent of each other given the class:

  15. C X1 X2 X3 X4 X5 X6 Learning the Model • Common practice:maximum likelihood • simply use the frequencies in the data

  16. Using Naive Bayes Classifiers to Classify Text: Basic method • Attributes are text positions, values are words. • Still too many possibilities • Assume that classification is independent of the positions of the words • Use same parameters for each position

  17. Text Classification Algorithms: Learning • From training corpus, extract Vocabulary • Calculate required P(cj)and P(xk | cj)terms • For each cj in Cdo • docsjsubset of documents for which the target class is cj • Textj single document containing all docsj • for each word xkin Vocabulary • nk number of occurrences ofxkin Textj

  18. Text Classification Algorithms: Classifying • positions  all word positions in current document which contain tokens found in Vocabulary • Return cNB, where

  19. Naïve Bayes Posterior Probabilities • Classification results of naïve Bayes (the class with maximum posterior probability) are usually fairly accurate. • However, due to the inadequacy of the conditional independence assumption, the actual posterior-probability numerical estimates are not. • Output probabilities are generally very close to 0 or 1.

  20. Feature selection via Mutual Information • We might not want to use all words, but just reliable, good discriminators • In training set, choose k words which best discriminate the categories. • One way is in terms of Mutual Information: • For each word w and each category c

  21. OTHER APPROACHES TO FEATURE SELECTION • T-TEST • CHI SQUARE • TF/IDF (CFR. IR lectures) • Yang & Pedersen 1997: eliminating features leads to improved performance

  22. NAÏVE BAYES NOT SO NAIVE • Naïve Bayes: First and Second place in KDD-CUP 97 competition, among 16 (then) state of the art algorithms Robust to Irrelevant Features Irrelevant Features cancel each other without affecting results Instead Decision Trees & Nearest-Neighbor methods can heavily suffer from this. • Very good in Domains with many equally important features Decision Trees suffer from fragmentation in such cases – especially if little data • A good dependable baseline for text classification (but not the best)! • Optimal if the Independence Assumptions hold: • If assumed independence is correct, then it is the Bayes Optimal Classifier for problem • Very Fast: • Learning with one pass over the data; testing linear in the number of attributes, and document collection size • Low Storage requirements • Handles Missing Values

  23. PANTEL AND LIN: SPAMCOP • Uses a Naïve Bayes classifier • M is spam if P(Spam|M) > P(NonSpam|M) • Method • Tokenize message using Porter Stemmer • Estimate P(W|C) using m-estimate (a form of smoothing) • Remove words that do not satisfy certain conditions • Train: 160 spams, 466 non-spams • Test: 277 spams, 346 non-spams • Results: ERROR RATE of 4.33% • Worse results using trigrams


  25. REFERENCES • Mosteller, F., & Wallace, D. L. (1984). Applied Bayesian and Classical Inference: the Case of the Federalist Papers (2nd ed.). New York: Springer-Verlag. • P. Pantel and D. Lin, 1998. “SPAMCOP: A Spam classification and organization program”, In Proc. Of the 1998 workshop on learning for text categorization, AAAI • Sebastiani, F., 2002, “Machine Learning in Automated Text Categorization”, ACM Computing Surveys, 34(1), 1-47