1. Distance functions and IE – 4? William W. Cohen CALD

2. Announcements Current statistics: –days with unscheduled student talks: 6 –students with unscheduled student talks: 4 –Projects are due: 4/28 (last day of class) –Additional requirement: draft (for comments) no later than 4/21

3. The data integration problem

4. String distance metrics so far... Term-based (e.g. TF/IDF as in WHIRL) –Distance depends on set of words contained in both s and t – so sensitive to spelling errors. –Usually weight words to account for “importance” –Fast comparison: O(n log n) for |s|+|t|=n Edit-distance metrics –Distance is shortest sequence of edit commands that transform s to t. –No notion of word importance –More expensive: O(n 2 ) Other metrics –Jaro metric & variants –Monge-Elkan’s recursive string matching –etc? Which metrics work best, for which problems?

5. Jaro metric

6. Winkler-Jaro metric

8. String distance metrics so far... Term-based (e.g. TF/IDF as in WHIRL) –Distance depends on set of words contained in both s and t – so sensitive to spelling errors. –Usually weight words to account for “importance” –Fast comparison: O(n log n) for |s|+|t|=n Edit-distance metrics –Distance is shortest sequence of edit commands that transform s to t. –No notion of word importance –More expensive: O(n 2 ) Other metrics –Jaro metric & variants –Monge-Elkan’s recursive string matching –etc? Which metrics work best, for which problems?

9. So which metric should you use? Java toolkit of string-matching methods from AI, Statistics, IR and DB communities Tools for evaluating performance on test data Exploratory tool for adding, testing, combining string distances –e.g. SecondString implements a generic “Winkler rescorer” which can rescale any distance function with range of [0,1] URL – http://secondstring.sourceforge.net Distribution also includes several sample matching problems. SecondString (Cohen, Ravikumar, Fienberg):

10. SecondString distance functions Edit-distance like: –Levenshtein – unit costs –untuned Smith-Waterman –Monge-Elkan (tuned Smith-Waterman) –Jaro and Jaro-Winkler

11. Results - Edit Distances Monge-Elkan is the best on average....

12. Edit distances

13. SecondString distance functions Term-based, for sets of terms S and T: –TFIDF distance –Jaccard distance: –Language models: construct P S and P T and use

14. SecondString distance functions Term-based, for sets of terms S and T: –TFIDF distance –Jaccard distance –Jensen-Shannon distance smoothing toward union of S,T reduces cost of disagreeing on common terms unsmoothed P S, Dirichlet smoothing, Jelenik-Mercer – “Simplified Fellegi-Sunter”

16. Results – Token Distances

18. SecondString distance functions Hybrid term-based & edit-distance based: –Monge-Elkan’s “recursive matching scheme”, segmenting strings at token boundaries (rather than separators like commas) –SoftTFIDF Like TFIDF but consider not just tokens in both S and T, but tokens in S “close to” something in T (“close to” relative to some distance metric) Downweight close tokens slightly

20. Results – Hybrid distances

21. Results - Overall

23. Prospective test on two clustering tasks

24. An anomolous dataset

25. An anomalous dataset: census

26. Why?

27. Other results with SecondString Distance functions over structured data records (first name, last name, street, house number) Learning to combine distance functions Unsupervised/semi-supervised training for distance functions over structured data

28. Combining Information Extraction and Similarity Computations 2) Krauthammer et al 1) Bunescu et al

29. Experiments Hand-tagged 50 abstracts for gene/protein entities (pre-selected to be about human genes) Collected dictionary of 40,000+ protein names from on-line sources –not complete –example matching is not sufficient Approach: use hand-coded heuristics to propose likely generalizations of existing dictionary entries. –not hand-coded or off-the-shelf similarity metrics

30. Example name generalizations

31. Basic idea behind the algorithm original dictionary carefully-tuned heuristics (aka hacks) similar (but not identical process) applied to word n- grams from text to do IE: extract if n-gram -> CD

32. Example: canonicalizing “short names” (different procedure for “full names” and “one-word” names)

33. NF-25 in OD NF Nf “... NF-kappa B...”NF NF in CD? ( ) NF => CD (from ) Recognize:

34. Results Why is precision less than 100%? When should you use “similarity by normalization”? Could a simpler algorithm do as well? Is there overfitting? (50 abstracts, <750 proteins)

35. ...

36. Combining Information Extraction and Similarity Computations 2) Krauthammer et al 1) Bunescu et al

37. Background Common task in proteomics/genomics: –look for (soft) matches to a query sequence in a large “database” of sequences. –want to find subsequences (genes) that are highly similar (and hence probably related) –want to ignore “accidental” matches –possible technique is Smith-Waterman (local alignment) want char-char “reward” for alignment to reflect confidence that the alignment is not due to chance

38. Background Common task in proteomics/genomics: –look for (soft) matches to a query sequence in a large “database” of sequences. –want to find subsequences (genes) that are highly similar (and hence probably related) –want to ignore “accidental” matches –possible technique is Smith-Waterman (local alignment) want char-char “reward” for alignment to reflect confidence that the alignment is not due to chance

39. Smith-Waterman distance c o h e n d o r f m 0 0 0 0 0 0 0 0 0 c 1 0 0 0 0 0 0 0 0 c 0 0 0 0 0 0 0 0 0 o 0 2 1 0 0 0 2 1 0 h 0 1 4 3 2 1 1 1 0 n 0 0 3 3 5 4 3 2 1 s 0 0 2 2 4 4 3 2 1 k 0 0 1 1 3 3 3 2 1 i 0 0 0 0 2 2 2 2 1 dist=5

40. In general “peaks” in the matrix scores indicate highly similar substrings.

41. Background Common task in proteomics/genomics: –look for (soft) matches to a query sequence in a large “database” of sequences. –possible technique is Smith-Waterman (local alignment) want char-char “reward” for alignment to reflect confidence that the alignment is not due to chance based on substitutability theory for amino acids –doesn’t scale well BLAST and FASTA: fast approximate S-W

42. BLAST/FASTA ideas Find all char n-grams (“words”) in the query string. FASTA: –Use inverted indices to find out where these words appear in the DB sequence –Use S-W only near DB sections that contain some of these words

43. BLAST/FASTA ideas Find all char n-grams (“words”) in the query string. BLAST: –Generate variations of these words by looking for changes that would lead to strong similarities –Discard “low IDF” words (where accidental matches are likely) –Use expanded set of n-grams to focus search

44. query string words and expansions

45. BLAST/FASTA ideas Find all char n-grams (“words”) in the query string. BLAST: –Generate variations of these words by looking for changes that would lead to strong similarities –Discard “low IDF” words (where accidental matches are likely) –Use expanded set of n-grams to focus search The BLAST program: –Widely used, –Fast implementation, –Supports asking multiple queries against a database at once... –Can one use it find soft matches of protein names (from a dictionary) in text?

46. Basic idea: Protein database Query strings Proposed alignment (query->database) Query algorithm: BLAST Biomedical paper Protein name dictionary Extracted protein name (dict. entry->text) IE system: dictionaries+BLAST (optimized for this problem)

47. 1) Mapping text to DNA sequences (Q: what sort of char similarity is this?)

48. 2) Optimizing blast Split protein-name database into several parts (for short, medium-length, long protein names) Require space chars before and after “short” protein names. Manually search (grid search?) for better settings for certain key parameters for each protein-name subdatabase –With what data? Evaluate on one review article, 1162 protein names –inter-annotator agreement not great (70-85%)

49. 2) Optimizing blast

51. Results

52. Overall: precision 71.1%, recall 78.8% (opt)