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Text Classification

Text Classification

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Text Classification

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  1. Text Classification Chapter 2 of “Learning to Classify Text Using Support Vector Machines” by Thorsten Joachims, Kluwer, 2002.

  2. Text Classification (TC) : Definition • Infer a classification rule from a sample of labelled training documents (training set) so that it classifies new examples (test set) with high accuracy. • Using the “ModApte” split, the ratio of training documents to test documents is 3:1

  3. Three settings • Binary setting (simplest). Only two classes, e.g. “relevant” and “non-relevant” in IR, “spam” vs. “legitimate” in spam filters. • Multi-class setting, e.g. email routing at a service hotline to one out of ten customer representatives, Can be reduced into binary tasks: “one against the rest” strategy. • Multi-label setting – e.g. semantic topic identifiers for indexing news articles. An article can be in one, many, or no categories. Can also be split into a set of binary classification tasks.

  4. Representing text as example vectors • The basic blocks for representing text will be called indexing terms • Word-based are most common. Very effective in IR, even though words such as “bank” have more than one meaning. • Advantage of simplicity – split the input text into words by white space. • Assume the ordering of words is irrelevant – the “bag of words” model. Only the frequency of each word in the document is recorded. • “bag of words” model ensures that each document is represented by a vector of fixed dimensionality. Each component of the vector represents the value (e.g. the frequency of that word in that document, TF) of one attribute.

  5. Other levels of text representation • More sophisticated representations than the “bag-of-words” have not yet shown consistent and substantial improvements • Sub-word level, e.g. n-grams are robust against spelling errors. See Kjell’s neural network. • Multi-word level. May use syntactic phrase indexing such as noun phrases (e.g. adjective-noun) followed by co-occurrence patterns (e.g. speed limit) • Semantic level. Latent Semantic Indexing (LSI) aims to automatically generate semantic categories based on a bag of words representation. Another approach would make use of thesauri.

  6. Feature Selection • To remove irrelevant or inappropriate attributes from the representation. • Advantages are protection against over-fitting, and increased computational efficiency with fewer dimensions to work with. • 2 most common strategies: • a) Feature subset selection: use a subset of the original features • b) Feature construction: new features are introduced by combining original features.

  7. Feature subset selection techniques • Stopword elimination (removes high frequency words) • Document frequency thresholding (remove infrequent words, e.g. those occurring less than m times in the training corpus) • Mutual information • Chi-squared test (X²) • But: an appropriate learning algorithm should be able to detect irrelevant features as part of the learning process.

  8. Mutual Information • We consider the association between a term t and a category c. How often do they occur together, compared with how common the term is, and how common is membership of the category? • A is the number of times t occurs in c • B is the number of times t occurs outside c • C is the number of times t does not occur in c • D is the number of times t does not occur outside c • N = A + B + C + D. • MI(t,c) = log (A.N / ((A + C)(A + B)) ) • If MI > 0 then there is a positive association between t and c • If MI = 0 there is no association between t and c • If MI < 0 then t and c are in complementary distribution • Units of MI are bits of information.

  9. Chi-squared measure (X²) • X²(t,c) = N.(AD-CB)² / (A+C).(B+D).(A+B).(C+D). • E.g. X² for words in US as opposed to UK English (1990s) • percent 485.2; U 383.3; toward 327.0; program 324.4; Bush 319.1; Clinton 316.8; President 273.2; programs 262.0; American 224.9; S 222.0. • These feature subset selection methods do not allow for dependencies between words, e.g. “click here”. • See Yang and Pedersen (1997), A Comparative Study on Feature Selection in Text Categorisation.

  10. Term Weighting • A “soft” form of feature selection. • Does not remove attributes, but adjusts their relative influence. • Three components: • Document component (e.g. binary, present in document = 1, absent = 0; term frequency (TF)) • Collection component (e.g. inverse document frequency log (N / DF)) • Normalisation component, so that large and small documents can be compared on the same scale e.g. 1 / sqrt(sum of xj²) • The final weight is found by multiplying the 3 components

  11. Feature Construction • The new features should represent most of the information in the original representation while minimising the number of attributes. • Examples of techniques are: • Stemming • Thesauri group words into semantic categories, e.g. synonyms can be placed in equivalence classes. • Latent Semantic Indexing • Term clustering

  12. Learning Methods • Naïve Bayes classifier • Rocchio algorithm • K-nearest neighbours • Decision tree classifier • Neural Nets • Support Vector Machines

  13. Naïve Bayesian Model (1) • Spam Filter example from Sahimi et al. • Odds(Rel|x) = Odds(Rel) * Pr(x|Rel) / Pr(x|NRel) • Pr(“cheap” “v1agra” “NOW!” | spam) = Pr(“cheap”|spam) * Pr(“v1agra”|spam) * Pr(“NOW!”|spam) • Only classify as spam if odds > 100 – 1 on.

  14. Naïve Bayesian model (2) • Sahimi et al. use word indicators, and also the following non-word indicators: • Phrases: free money, only $, over 21 • Punctuation: !!!! • Domain name of sender: .edu less likely to be spam than .com • Junk mail more likely to be sent at night than legitimate mail. • Is recipient an individual user or a mailing list?

  15. Our Work on the Enron Corpus- The PERC (George Ke) • Find a centroid cifor each category Ci • For each test document x: • Find k nearest neighbouring training documents to x • Similarity between x and the training document dj is added to similarity between x and ci • Sort similarity scores sim(x,Ci) in descending order • Decision to assign x to Cican be made using various thresholding strategies

  16. Rationale for the PERC Hybrid Approach • Centroid method overcomes data sparseness: emails tend to be short. • kNN allows the topic of a folder to drift over time. Considering the vector space locally allows matching against features which are currently dominant.

  17. aa w11 ab h1 o1 (Shakespeare) ac h2 o2 (Marlowe) ad h3 ae input layer “hidden” layer output layer Kjell: A Stylometric Multi-Layer Perceptron

  18. Performance Measures (PM) • PM used for evaluating TC are often different from those optimised by the learning algorithms. • Loss-based measures (error rate and cost models). • Precision and recall-based measures.

  19. Error Rate and Asymmetric Cost • Error Rate is defined as the probability of the classification rule predicting the wrong class, • Err = (f+- + f-+) / (f++ + f+- + f-+ + f--) • Problem: negative examples tend to outnumber positive examples. So if we always guess “not in category”, it seems that we have a very low error rate. • For many applications, predicting a positive example correctly is of higher utility than predicting a negative example correctly. • We can incorporate this into the performance measure using a cost (or inversely, utility) matrix: • Err = (C++f++ + C+-f+- + C-+f-+ + C--f--) / (f++ + f+- + f-+ + f--)

  20. Precision and Recall • The Recall of a classification rule is the probability that a document that should be in the category is classified correctly • R = f++ / (f++ + f-+) • Precision is the probability that a document classified into a category is indeed classified correctly • P = f++ / (f++ + f+-) • F = 2PR / (P + R) if P and R are equally important

  21. Micro- and macro- averaging • Often it is useful to compute the average performance of a learning algorithm over multiple training/test sets or multiple classification tasks. • In particular for the multi-label setting, one is usually interested in how well all the labels can be predicted, not only a single one. • This leads to the question of how the results of m binary tasks can be averaged to get a single performance value. • Macro-averaging: the performance measure (e.g. R or P) is computed separately for each of the m experiments. The average is computed as the arithmetic mean of the measure over all experiments • Micro-averaging: instead average the contingency tables found for each of m experiments, to produce f++(ave), f+-(ave), f-+(ave), f--(ave). For recall, this implies • R(micro) = f++(ave) / (f++ + f-+)