1. Using the Web as an Implicit Training Set: Application to Structural Ambiguity Resolution Preslav Nakov and Marti Hearst Computer Science Division and SIMS University of California, Berkeley Supported by NSF DBI-0317510 and a gift from Genentech

2. Motivation Huge datasets trump sophisticated algorithms. “Scaling to Very Very Large Corpora for Natural Language Disambiguation”, ACL 2001 (Banko & Brill, 2001) Task: spelling correction Raw text as “training data” Log-linear improvement even to billion words  Getting more data is better than fine-tuning algorithms. How to generalize to other problems?

3. Web as a Baseline “Web as a baseline” (Lapata & Keller 04;05): applied simple n-gram models to: machine translation candidate selection article generation noun compound interpretation noun compound bracketing adjective ordering spelling correction countability detection prepositional phrase attachment All unsupervised Findings: Sometimes rival best supervised approaches. => Web n-grams should be used as a baseline. Significantly better than the best supervised algorithm. Not significantly different from the best supervised algorithm.

4. Our Contribution Potential of these ideas is not yet fully realized We introduce new features paraphrases surface features Applied to structural ambiguity problems Data sparseness: need statistics for every possible word and for word combinations Problems (unsupervised): Noun compound bracketing PP attachment NP coordination state-of-the-art results (Nakov&Hearst, 2005) this work

5. Task 1: Prepositional Phrase Attachment

6. PP attachment (a) Peter spent millions of dollars.(noun) (b) Peter spent time with his family.(verb) quadruple: (v, n1, p, n2) (a)(spent, millions, of, dollars) (b)(spent, time, with, family) Human performance: quadruple: 88% whole sentence: 93% PP combines with the NP to form another NP PP is an indirect object of the verb

7. Related Work Supervised (Brill & Resnik, 94): transformation-based learning, WordNet classes, P=82% (Ratnaparkhi & al., 94): ME, word classes (MI), P=81.6% (Collins & Brooks, 95): back-off, P=84.5% (Stetina & Makoto, 97): decision trees, WordNet, P=88.1% (Toutanova & al., 04): morphology, syntax, WordNet, P=87.5% (Olteanu & Moldovan, 05): in context, parser, FrameNet, Web, SVM, P=92.85% Unsupervised (Hindle & Rooth, 93): partially parsed corpus, lexical associations over subsets of (v,n1,p), P=80%,R=80% (Ratnaparkhi, 98): POS tagged corpus, unambiguous cases for (v,n1,p), (n1,p,n2), classifier: P=81.9% (Pantel & Lin,00): collocation database, dependency parser, large corpus (125M words), P=84.3% Unsup. state-of-the-art Ratnaparkhi dataset

8. Related Work: Web Unsup. (Volk, 00): Altavista, NEAR operator, German, compare Pr(p|n1) vs. Pr(p|v), P=75%, R=58% (Volk, 01): Altavista, NEAR operator, German, inflected queries, Pr(p,n2|n1) vs. Pr(p,n2|v), P=75%, R=85% (Calvo & Gelbukh, 03): exact phrases, Spanish, P=91.97%, R=89.5% (Lapata & Keller,05): Web n-grams, English, Ratnaparkhi dataset, P in low 70’s (Olteanu & Moldovan, 05): supervised, English, in context, parser, FrameNet, Web counts, SVM, P=92.85%

9. PP-attachment: Our Approach Unsupervised (v,n1,p,n2) quadruples, Ratnaparkhi test set Google and MSN Search Exact phrase queries Inflections: WordNet 2.0 Adding determiners where appropriate Models: n-gram association models Web-derived surface features paraphrases

10. Probabilities: Estimation Using page hits as a proxy for n-gram counts Pr(w 1 |w 2 ) = #(w 1, w 2 ) / #(w 2 ) #(w 2 ) word frequency; query for “w 2 ” #(w 1, w 2 ) bigram frequency; query for “w 1 w 2 ” Pr(w 1,w 2 |w 3 ) = #(w 1, w 2, w 3 ) / #(w 3 )

11. N-gram models (i) Pr(p|n1) vs. Pr(p|v) (ii) Pr(p,n2|n1) vs. Pr(p,n2|v) I eat/v spaghetti/n1 with/p a fork/n2. I eat/v spaghetti/n1 with/p sauce/n2. Pr or # (frequency) smoothing as in (Hindle & Rooth, 93) back-off from (ii) to (i) N-grams unreliable, if n1 or n2 is a pronoun. MSN Search: no rounding of n-gram estimates

12. Web-derived Surface Features Example features open the door / with a key  verb (100.00%, 0.13%) open the door (with a key)  verb (73.58%, 2.44%) open the door – with a key  verb (68.18%, 2.03%) open the door, with a key  verb (58.44%, 7.09%) eat Spaghetti with sauce  noun (100.00%, 0.14%) eat ? spaghetti with sauce  noun (83.33%, 0.55%) eat, spaghetti with sauce  noun (65.77%, 5.11%) eat : spaghetti with sauce  noun (64.71%, 1.57%) Summing achieves high precision, low recall. PRPR sum compare

13. Paraphrases v n1 p n2 v n2 n1(noun) v p n2 n1(verb) p n2 * v n1(verb) n1 p n2 v(noun) v PRONOUN p n2(verb) BE n1 p n2(noun)

14. Paraphrases: pattern (1) (1) v n1 p n2  v n2 n1(noun) Can we turn “n1 p n2” into a noun compound “n2 n1”? meet/v demands/n1 from/p customers/n2  meet/v the customer/n2 demands/n1 Problem: ditransitive verbs like give gave/v an apple/n1 to/p him/n2  gave/v him/n2 an apple/n1 Solution: no determiner before n1 determiner before n2 is required the preposition cannot be to

15. Paraphrases: pattern (2) (2) v n1 p n2  v p n2 n1(verb) If “p n2” is an indirect object of v, then it could be switched with the direct object n1. had/v a program/n1 in/p place/n2  had/v in/p place/n2 a program/n1 Determiner before n1 is required to prevent “n2 n1” from forming a noun compound.

16. Paraphrases: pattern (3) (3) v n1 p n2  p n2 * v n1(verb) “*” indicates a wildcard position (up to three intervening words are allowed) Looks for appositions, where the PP has moved in front of the verb, e.g. I gave/v an apple/n1 to/p him/n2  to/p him/n2 I gave/v an apple/n1

17. Paraphrases: pattern (4) (4) v n1 p n2  n1 p n2 v(noun) Looks for appositions, where “n1 p n2” has moved in front of v shaken/v confidence/n1 in/p markets/n2  confidence/n1 in/p markets/n2 shaken/v

18. Paraphrases: pattern (5) (5) v n1 p n2  v PRONOUN p n2(verb) n1 is a pronoun  verb (Hindle&Rooth, 93) Pattern (5) substitutes n1 with a dative pronoun (him or her), e.g. put/v a client/n1 at/p odds/n2  put/v him at/p odds/n2 pronoun

19. Paraphrases: pattern (6) (6) v n1 p n2  BE n1 p n2(noun) BE is typically used with a noun attachment Pattern (6) substitutes v with a form of to be (is or are), e.g. eat/v spaghetti/n1 with/p sauce/n2  is spaghetti/n1 with/p sauce/n2 to be

20. Evaluation Ratnaparkhi dataset 3097 test examples, e.g. prepare dinner for family V shipped crabs from province V n1 or n2 is a bare determiner: 149 examples problem for unsupervised methods left chairmanship of the N is the of kind N acquire securities for an N special symbols: %, /, & etc.: 230 examples problem for Web queries buy % for 10 V beat S&P-down from % V is 43%-owned by firm N

21. Results Simpler but not significantly different from 84.3% (Pantel&Lin,00). For prepositions other then OF. (of  noun attachment) Smoothing is not needed on the Web Models in bold are combined in a majority vote. Checking directly for...

22. Task 2: Coordination

23. Coordination & Problems (Modified) real sentence: The Department of Chronic Diseases and Health Promotion leads and strengthens global efforts to prevent and control chronic diseases or disabilities and to promote health and quality of life. Problems: boundaries: words, constituents, clauses etc. interactions with PPs: [health and [quality of life]] vs. [[health and quality] of life] or meaning and: chronic diseases or disabilities ellipsis

24. NC coordination: ellipsis Ellipsis car and truck production means car production and truck production No ellipsis president and chief executive All-way coordination Securities and Exchange Commission

25. NC Coordination: ellipsis Quadruple (n1,c,n2,h) Penn Treebank annotations ellipsis: (NP car/NN and/CC truck/NN production/NN). no ellipsis: (NP (NP president/NN) and/CC (NP chief/NN executive/NN)) all-way: can be annotated either way This is a problem a parser must deal with. Collins’ parser always predicts ellipsis, but other parsers (e.g. Charniak’s) try to solve it.

26. Related Work (Resnik, 99): similarity of form and meaning, conceptual association, decision tree, P=80%, R=100% (Rus & al., 02): deterministic, rule-based bracketing in context, P=87.42%, R=71.05% (Chantree & al., 05): distributional similarities from BNC, Sketch Engine (freqs., object/modifier etc.), P=80.3%, R=53.8% (Goldberg, 99): different problem (n1,p,n2,c,n3), adapts Ratnaparkhi (99) algorithm, P=72%, R=100%

27. N-gram models (n1,c,n2,h) (i) #(n1,h) vs. #(n2,h) (ii) #(n1,h) vs. #(n1,c,n2)

28. Surface Features sum compare

29. Paraphrases n1 c n2 h n2 c n1 h(ellipsis) n2 h c n1(NO ellipsis) n1 h c n2 h(ellipsis) n2 h c n1 h(ellipsis)

30. Paraphrases: Pattern (1) (1) n1 c n2 h  n2 c n1 h(ellipsis) Switch places of n1 and n2 bar/n1 and/c pie/n2 graph/h  pie/n2 and/c bar/n1 graph/h

31. Paraphrases: Pattern (2) (2) n1 c n2 h  n2 h c n1(NO ellipsis) Switch places of n1 and n2 h president/n1 and/c chief/n2 executive/h  chief/n2 executive/h and/c president/n1

32. Paraphrases: Pattern (3) (3) n1 c n2 h  n1 h c n2 h(ellipsis) Insert the elided head h bar/n1 and/c pie/n2 graph/h  bar/n1 graph/h and/c pie/n2 graph/h h

33. Paraphrases: Pattern (4) (4) n1 c n2 h  n2 h c n1 h(ellipsis) Insert the elided head h, but also switch n1 and n2 bar/n1 and/c pie/n2 graph/h  pie/n2 graph/h and/c bar/n1 graph/h h

34. (Rus & al.,02) Heuristics Heuristic 1: no ellipsis n1=n2 milk/n1 and/c milk/n2 products/h Heuristic 4: no ellipsis n1 and n2 are modified by an adjective Heuristic 5: ellipsis only n1 is modified by an adjective Heuristic 6: no ellipsis only n2 is modified by an adjective We use a determiner.

35. Number Agreement Introduced by Resnik (93) (a) n1&n2 agree, but n1&h do not  ellipsis; (b) n1&n2 don’t agree, but n1&h do  no ellipsis; (c) otherwise leave undecided.

36. Results 428 examples from Penn TB Models in bold are combined in a majority vote. Comparable to other researchers (but no standard dataset). Bad, compares bigram to trigram.

37. Conclusions & Future Work Tapping the potential of very large corpora for unsupervised algorithms Go beyond n-grams Surface features Paraphrases Results competitive with best unsupervised Results can rival supervised algorithms’ Future Work other NLP tasks better evidence combination There should be even more exciting features on the Web!

38. The End Thank you!