text categorization updated 11/1/2006. performance measures – binary classification accuracy: acc...
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text categorization
Updated 11/1/2006
Performance measures – binary classification
Accuracy: acc = (a+d)/(a+b+c+d)
Precision: p = a/(a+b) Recall: r = a/(a+c) F F = (2+1) pr/(2p +r)
Ususally one uses F1 = 2pr/(p +r) Break-even point
Ground truth
True False
True a b
False c d
Cla
ssifi
er
ass
ign
ed
Contigency table
Performance measures – multiple categories
Micro averaging Macro averaging
Reuters 21578 Reuters collection contains 9603 training
articles and 3299 test articles. Were sent over the Reuters newswire in 1987. Contains about 100 categories such as
‘mergers and acquisitions’, ‘interset rates’, ‘wheat’, ‘silver’ etc.
Distribution of articles among categories is highly non-uniform.
‘earning’ contains 2709 docs 75 categories contain less than 10 docs each.
Example of a Reuters news story from category ‘earning’<DATE>26-FEB-1987 15:18:59.34</DATE><TOPICS><D>earn</D></TOPICS><TEXT><TITLE>COBANCO INC <CBCO> YEAR NET</TITLE><DATELINE> SANTA CRUZ, Calif., Feb 26 - </DATELINE><BODY>Shr 34 cts vs 1.19 dlrs Net 807,000 vs 2,858,000 Assets 510.2 mln vs 479.7 mln Deposits 472.3 mln vs 440.3 mln Loans 299.2 mln vs 327.2 mln Note: 4th qtr not available. Year includes 1985 extraordinary
gain from tax carry forward of 132,000 dlrs, or five cts per shr. Reuter</BODY></TEXT></REUTERS>
Categorization methods Decision trees Naïve bayes K-nearest neighbors (KNN) Neural networks Support Vector Machines (SVM)
Representation of documents The most popular representation is ‘Bag of
Words’, which ignores all structure of documents.
Document I will be represented by a vector Xi Rn (n is the number of word types), where the j’th coordinate is just the number of times word wj appears in the document. (so called ‘term frequency – tfj).
Decision trees
1607/1704 = 0.943 694/5977 = 0.116
Earnings?
2301/7681 = 0.3 of all docs
contains “cents” < 2 times contains “cents” 2 times
contains “versus” < 2 times
contains “versus”
2 times
contains “net”
< 1 time
contains “net”
1 time
1398/1403 = 0.996
209/301 = 0.694
“yes”
422/541 = 0.780
272/5436 = 0.050“no”
Building decision trees Information gain
Decision Tree Pruning
Naïve bayes Multivariate Bernoulli model Multinomial model
Precision recall curve
K-nearest neighbor
Neural network Perceptrons Multi-layer perceptrons
SVM
reuters 21578 – comparison*
*Yiming-Yang & Xin Liu, A re-examination of text categorization methods, SIGIR99)
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