1. Distance functions and IE William W. Cohen CALD

2. Announcements March 25 Thus – talk from Carlos Guestrin (Assistant Prof in Cald as of fall 2004) on max-margin Markov nets –time constraints? Writeups: –nothing today –“distance metrics for text” – three papers due next Monday, 3/22

3. Record linkage: definition Record linkage: determine if pairs of data records describe the same entity –I.e., find record pairs that are co-referent –Entities: usually people (or organizations or…) –Data records: names, addresses, job titles, birth dates, … Main applications: –Joining two heterogeneous relations –Removing duplicates from a single relation

4. Record linkage: terminology The term “record linkage” is possibly co- referent with: –For DB people: data matching, merge/purge, duplicate detection, data cleansing, ETL (extraction, transfer, and loading), de-duping –For AI/ML people: reference matching, database hardening, object consolidation, –In NLP: co-reference/anaphora resolution –Statistical matching, clustering, language modeling, …

5. Motivation Q: What does this have to do with IE? A: Quite a lot, actually... Webfind (Monge & Elkan, 1995)

6. Finding a technical paper c. 1995 Start with citation: " Experience With a Learning Personal Assistant", T.M. Mitchell, R. Caruana, D. Freitag, J. McDermott, and D. Zabowski, Communications of the ACM, Vol. 37, No. 7, pp. 81-91, July 1994. Find author’s institution (w/ INSPEC) Find web host (w/ NETFIND) Find author’s home page and (hopefully) the paper by browsing

7. Automatically finding a technical paper c. 1995 with WebFind Start with citation: " Experience With a Learning Personal Assistant", T.M. Mitchell, R. Caruana, D. Freitag, J. McDermott, and D. Zabowski, Communications of the ACM, Vol. 37, No. 7, pp. 81-91, July 1994. Find author’s institution (w/ automated search against INSPEC) Find web host (w/ auto search on NETFIND) Find author’s home page and (hopefully) the paper by heuristic spidering

8. The data integration problem

9. Control flow (modulo details about querying –Extract (author, department) pairs from DB1 –Extract (department,www server) pairs from DB2 –Execute the two-step plan to get paper: author -> department -> wwwServer –two steps means matching (linking, integrating, deduping,....) department names in DB1/DB2 –issues are completely different if user is executing a one-step plan: one-step plan is retrieval

10. String distance metrics: Levenshtein Edit-distance metrics –Distance is shortest sequence of edit commands that transform s to t. –Simplest set of operations: Copy character from s over to t Delete a character in s (cost 1) Insert a character in t (cost 1) Substitute one character for another (cost 1) –This is “Levenshtein distance”

11. Levenshtein distance - example distance(“William Cohen”, “Willliam Cohon”) WILLIAM_COHEN WILLLIAM_COHON CCCCICCCCCCCSC 00001111111122 s t op cost alignment

12. Levenshtein distance - example distance(“William Cohen”, “Willliam Cohon”) WILLIAM_COHEN WILLLIAM_COHON CCCCICCCCCCCSC 00001111111122 s t op cost alignment gap

13. Computing Levenshtein distance - 1 D(i,j) = score of best alignment from s1..si to t1..tj = min D(i-1,j-1), if si=tj //copy D(i-1,j-1)+1, if si!=tj //substitute D(i-1,j)+1 //insert D(i,j-1)+1 //delete

14. Computing Levenshtein distance - 2 D(i,j) = score of best alignment from s1..si to t1..tj = min D(i-1,j-1) + d(si,tj) //subst/copy D(i-1,j)+1 //insert D(i,j-1)+1 //delete (simplify by letting d(c,d)=0 if c=d, 1 else) also let D(i,0)=i (for i inserts) and D(0,j)=j

15. Computing Levenshtein distance - 3 D(i,j)= min D(i-1,j-1) + d(si,tj) //subst/copy D(i-1,j)+1 //insert D(i,j-1)+1 //delete COHEN M12345 C12345 C22345 O32345 H43234 N54333 = D(s,t)

16. Computing Levenshtein distance – 4 D(i,j) = min D(i-1,j-1) + d(si,tj) //subst/copy D(i-1,j)+1 //insert D(i,j-1)+1 //delete COHEN M 1 2345 C 1 2345 C 2 3345 O 3 2 345 H 43 2 3 4 N 5433 3 A trace indicates where the min value came from, and can be used to find edit operations and/or a best alignment (may be more than 1)

17. Needleman-Wunch distance D(i,j) = min D(i-1,j-1) + d(si,tj) //subst/copy D(i-1,j) + G //insert D(i,j-1) + G //delete d(c,d) is an arbitrary distance function on characters (e.g. related to typo frequencies, amino acid substitutibility, etc) William Cohen Wukkuan Cigeb G = “gap cost”

18. Smith-Waterman distance - 1 D(i,j) = max 0 //start over D(i-1,j-1) - d(si,tj) //subst/copy D(i-1,j) - G //insert D(i,j-1) - G //delete Distance is maximum over all i,j in table of D(i,j)

19. Smith-Waterman distance - 2 D(i,j) = max 0 //start over D(i-1,j-1) - d(si,tj) //subst/copy D(i-1,j) - G //insert D(i,j-1) - G //delete G = 1 d(c,c) = -2 d(c,d) = +1 COHEN M -2-3-4-5 C+1 0-2-3 C 0 0-2-3 O +2 +1 0 H -2+1 +4+3 +2 N -3 0+3 +5

20. Smith-Waterman distance - 3 D(i,j) = max 0 //start over D(i-1,j-1) - d(si,tj) //subst/copy D(i-1,j) - G //insert D(i,j-1) - G //delete G = 1 d(c,c) = -2 d(c,d) = +1 COHEN M 00000 C+1 0000 C 0 0000 O 0 +2 +1 00 H 0 +4+3 +2 N 0 0+3 +5

21. Smith-Waterman distance - 5 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

22. Smith-Waterman distance: Monge & Elkan’s WEBFIND (1996)

23. Smith-Waterman distance in Monge & Elkan’s WEBFIND (1996) Used a standard version of Smith-Waterman with hand-tuned weights for inserts and character substitutions. Split large text fields by separators like commas, etc, and explore different pairings (since S-W assigns a large cost to large transpositions) Result competitive with plausible competitors.

24. Smith-Waterman distance in Monge & Elkan’s WEBFIND (1996) String s=A 1 A 2... A K, string t=B 1 B 2... B L sim’ is editDistance scaled to [0,1] Monge-Elkan’s “recursive matching scheme” is average maximal similarity of A i to B j:

25. Smith-Waterman distance: Monge & Elkan’s WEBFIND (1996) computer science department stanford university palo alto california Dept. of Comput. Sci. Stanford Univ. CA USA 0.51 0.92 0.5 1.0

29. Results: S-W from Monge & Elkan

30. Affine gap distances Smith-Waterman fails on some pairs that seem quite similar: William W. Cohen William W. ‘Don’t call me Dubya’ Cohen Intuitively, a single long insertion is “cheaper” than a lot of short insertions Intuitively, are springlest hulongru poinstertimon extisn’t “cheaper” than a lot of short insertions

31. Affine gap distances - 2 Idea: –Current cost of a “gap” of n characters: nG –Make this cost: A + (n-1)B, where A is cost of “opening” a gap, and B is cost of “continuing” a gap.

32. Affine gap distances - 3 D(i,j) = max D(i-1,j-1) + d(si,tj) //subst/copy D(i-1,j)-1 //insert D(i,j-1)-1 //delete IS(i,j) = max D(i-1,j) - A IS(i-1,j) - B IT(i,j) = max D(i,j-1) - A IT(i,j-1) - B Best score in which si is aligned with a ‘gap’ Best score in which tj is aligned with a ‘gap’ D(i-1,j-1) + d(si,tj) IS(I-1,j-1) + d(si,tj) IT(I-1,j-1) + d(si,tj)

33. Affine gap distances - 4 -B -d(si,tj) D IS IT -d(si,tj) -A

34. Affine gap distances – experiments ( from McCallum,Nigam,Ungar KDD2000) Goal is to match data like this:

35. Affine gap distances – experiments ( from McCallum,Nigam,Ungar KDD2000) Hand-tuned edit distance Lower costs for affine gaps Even lower cost for affine gaps near a “.” HMM-based normalization to group title, author, booktitle, etc into fields

36. Affine gap distances – experiments TFIDFEdit Distance Adaptive Cora 0.7510.839 0.945 0.721 0.964 OrgName10.9250.6330.923 0.3660.9500.776 Orgname20.9580.5710.958 0.7780.9120.984 Restaurant0.9810.8271.000 0.9670.8670.950 Parks0.9760.9670.984 0.967