1. 1 SIMS 290-2: Applied Natural Language Processing Preslav Nakov Sept 29, 2004

2. 2 Today Feature selection TF.IDF Term Weighting Term Normalization

3. 3 Features for Text Categorization Linguistic features Words – lowercase? (should we convert to?) – normalized? (e.g. “texts”  “text”) Phrases Word-level n-grams Character-level n-grams Punctuation Part of Speech Non-linguistic features document formatting informative character sequences (e.g. &lt)

4. 4 If the algorithm cannot handle all possible features –e.g. language identification for 100 languages using all words –text classification using n-grams Good features can result in higher accuracy But! Why feature selection? What if we just keep all features? –Even the unreliable features can be helpful. –But we need to weight them:  In the extreme case, the bad features can have a weight of 0 (or very close), which is… a form of feature selection! When Do We Need Feature Selection?

5. 5 Why Feature Selection? Not all features are equally good! Bad features: best to remove –Infrequent  unlikely to be be met again  co-occurrence with a class can be due to chance –Too frequent  mostly function words –Uniform across all categories Good features: should be kept –Co-occur with a particular category –Do not co-occur with other categories The rest: good to keep

6. 6 Types Of Feature Selection?  Feature selection reduces the number of features  Usually:  Eliminating features  Weighting features  Normalizing features  Sometimes by transforming parameters  e.g. Latent Semantic Indexing using Singular Value Decomposition  Method may depend on problem type  For classification and filtering, may use information from example documents to guide selection

7. 7 Feature Selection Task independent methods Document Frequency (DF) Term Strength (TS) Task-dependent methods Information Gain (IG) Mutual Information (MI)  2 statistic (CHI) Empirically compared by Yang & Pedersen (1997)

8. 8 Pedersen & Yang Experiments Compared feature selection methods for text categorization 5 feature selection methods: –DF, MI, CHI, (IG, TS) –Features were just words 2 classifiers: –kNN: k-Nearest Neighbor (to be covered next week) –LLSF: Linear Least Squares Fit 2 data collections: –Reuters-22173 –OHSUMED: subset of MEDLINE (1990&1991 used)

9. 9 DF: number of documents a term appears in Based on Zipf’s Law Remove the rare terms: (met 1-2 times) Non-informative Unreliable – can be just noise Not influential in the final decision Unlikely to appear in new documents Plus Easy to compute Task independent: do not need to know the classes Minus Ad hoc criterion Rare terms can be good discriminators (e.g., in IR) Document Frequency (DF) What about the frequent terms? What is a “rare” term?

10. 10 Examples of Frequent Words: Most Frequent Words in Brown Corpus

11. 11 Common words from a predefined list Mostly from closed-class categories: –unlikely to have a new word added –include: auxiliaries, conjunctions, determiners, prepositions, pronouns, articles But also some open-class words like numerals Bad discriminators uniformly spread across all classes can be safely removed from the vocabulary –Is this always a good idea? (e.g. author identification) Stop Word Removal

12. 12  2 statistic (pronounced “kai square”) The most commonly used method of comparing proportions. Checks whether there is a relationship between being in one of two groups and a characteristic under study. Example: Let us measure the dependency between a term t and a category c. –the groups would be:  1) the documents from a category c i  2) all other documents –the characteristic would be:  “document contains term t”  2 statistic (CHI)

13. 13 Is “jaguar” a good predictor for the “auto” class? We want to compare: the observed distribution above; and null hypothesis: that jaguar and auto are independent  2 statistic (CHI) Term = jaguar Term  jaguar Class = auto2500 Class  auto 39500

14. 14 Under the null hypothesis: (jaguar and auto – independent): How many co-occurrences of jaguar and auto do we expect? We would have: P r (j,a) = P r (j)  P r (a) So, there would be: N  P r (j,a), i.e. N  P r (j)  P r (a) P r (j) = (2+3)/N; P r (a) = (2+500)/N; N=2+3+500+9500 Which is: N(5/N)(502/N)=2510/N=2510/10005  0.25  2 statistic (CHI) Term = jaguar Term  jaguar Class = auto2500 Class  auto 39500

15. 15 Under the null hypothesis: (jaguar and auto – independent): How many co-occurrences of jaguar and auto do we expect? We would have: P r (j,a) = P r (j)  P r (a) So, there would be: N  P r (j,a), i.e. N  P r (j)  P r (a) P r (j) = (2+3)/N; P r (a) = (2+500)/N; N=2+3+500+9500 Which is: N(5/N)(502/N)=2510/N=2510/10005  0.25  2 statistic (CHI) Term = jaguar Term  jaguar Class = auto2(0.25)500 Class  auto 39500 expected: f e observed: f o

16. 16 Under the null hypothesis: (jaguar and auto – independent): How many co-occurrences of jaguar and auto do we expect? We would have: P r (j,a) = P r (j)  P r (a) So, there would be: N  P r (j,a), i.e. N  P r (j)  P r (a) P r (j) = (2+3)/N; P r (a) = (2+500)/N; N=2+3+500+9500 Which is: N(5/N)(502/N)=2510/N=2510/10005  0.25  2 statistic (CHI) Term = jaguar Term  jaguar Class = auto2(0.25)500(502) Class  auto 3(4.75)9500(9498) expected: f e observed: f o

17. 17  2 is interested in (f o – f e ) 2 /f e summed over all table entries: The null hypothesis is rejected with confidence.999, since 12.9 > 10.83 (the value for.999 confidence).  2 statistic (CHI) Term = jaguar Term  jaguar Class = auto2(0.25)500(502) Class  auto 3(4.75)9500(9498) expected: f e observed: f o

18. 18 There is a simpler formula for  2 :  2 statistic (CHI) N = A + B + C + D A = #(t,c)C = #(¬t,c) B = #(t,¬c)D = #(¬t, ¬c)

19. 19 How to use  2 for multiple categories? Compute  2 for each category and then combine: we can require to discriminate well across all categories, then we need to take the expected value of  2 : or to discriminate well for a single category, then we take the maximum:  2 statistic (CHI)

20. 20 Plus normalized and thus comparable across terms  2 (t,c) is 0, when t and c are independent can be compared to  2 distribution, 1 degree of freedom Minus unreliable for low frequency terms computationally expensive  2 statistic (CHI)

21. 21 Information Gain A measure of importance of the feature for predicting the presence of the class. Defined as: The number of “bits of information” gained by knowing the term is present or absent Based on Information Theory –We won’t go into this in detail here. Plus: sound information theory justification Minus: computationally expensive

22. 22 Information Gain (IG) IG: number of bits of information gained by knowing the term is present or absent t is the term being scored, c i is a class variable entropy: H(c) specific conditional entropy H(c|t) specific conditional entropy H(c|¬t)

23. 23 The probability of seeing x followed by y vs. the probably of seeing x anywhere times the probability of seeing y anywhere. log ( P(x,y) / P(x)P(y) ) Mutual Information (MI)

24. 24 Approximation: Mutual Information (MI) A = #(t,c)C = #(¬t,c) B = #(t,¬c)D = #(¬t, ¬c) rare terms scored higher does not use term absence

25. 25 Compute MI for each category and then combine If we want to discriminate well across all categories, then we need to take the expected value of MI: To discriminate well for a single category, then we take the maximum: Using Mutual Information

26. 26 Mutual Information Plus I(t,c) is 0, when t and c are independent Sound information-theoretic interpretation Minus Small numbers produce unreliable results Computationally expensive Does not use term absence

27. 27 Mutual information Term strength

28. 28 DF, IG and CHI are good and strongly correlated thus using DF is good, cheap and task independent can be used when IG and CHI are too expensive MI is bad favors rare terms (which are typically bad) MI vs. IG Comparison: DF,TS,IG,CHI,MI mutual information gain

29. 29 Term Weighting In the study just shown, terms were (mainly) treated as binary features If a term occurred in a document, it was assigned 1 Else 0 Often it us useful to weight the selected features Standard technique: tf.idf

30. 30 TF: term frequency definition: TF = t ij –frequency of term i in document j purpose: makes the frequent words for the document more important IDF: inverted document frequency definition: IDF = log(N/n i ) –n i : number of documents containing term i –N : total number of documents purpose: makes rare words across documents more important TF.IDF definition: t ij  log(N/n i ) TF.IDF Term Weighting

31. 31 Term Normalization Combine different words into a single representation Stemming/morphological analysis –bought, buy, buys -> buy General word categories –$23.45, 5.30 Yen -> MONEY –1984, 10,000 -> DATE, NUM –PERSON –ORGANIZATION  (Covered in Information Extraction segment) Generalize with lexical hierarchies –WordNet, MeSH  (Covered later in the semester)

32. 32 Purpose: conflate morphological variants of a word to a single index term Stemming: normalize to a pseudoword –e.g. “more” and “morals” become “mor” (Porter stemmer) Lemmatization: convert to the root form –e.g. “more” and “morals” become “more” and “moral” Plus: vocabulary size reduction data sparseness reduction Minus: loses important features (even to_lowercase() can be bad!) questionable utility (maybe just “-s”, “-ing” and “-ed”?) Stemming & Lemmatization

33. 33 1.Feature selection infrequent term removal infrequent across the whole collection (i.e. DF) met in a single document most frequent term removal (i.e. stop words) 2.Normalization: 1.Stemming. (often) 2.Word classes (sometimes) 3.Feature weighting: TF.IDF or IDF 4.Dimensionality reduction. (occasionally) What Do People Do In Practice?

34. 34 Summary Feature selection Task independent methods: DF, TS Task dependent: IG, MI,  2 statistic Term weighting IDF TF.IDF Term normalization

35. 35 Feature Selection Yang Y., J. Pedersen. A comparative study on feature selection in text categorization. In J. D. H. Fisher, editor, The Fourteenth International Conference on Machine Learning (ICML'97), pages 412-420. Morgan Kaufmann, 1997. Term Weighting Salton G., C. Buckley, Term-weighting approaches in automatic text retrieval, Information Processing and Management: an International Journal, v.24 n.5, p.513- 523, 1988. Salton, G. 1989. Automatic text processing. Chapter 9. References