1 / 85

Weighted Parsing, Probabilistic Parsing

Weighted Parsing, Probabilistic Parsing. Our bane: Ambiguity. John saw Mary Typhoid Mary Phillips screwdriver Mary note how rare rules interact I see a bird is this 4 nouns – parsed like “city park scavenger bird”? rare parts of speech, plus systematic ambiguity in noun sequences

drea
Download Presentation

Weighted Parsing, Probabilistic Parsing

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Weighted Parsing, Probabilistic Parsing 600.465 - Intro to NLP - J. Eisner

  2. Our bane: Ambiguity • John saw Mary • Typhoid Mary • Phillips screwdriver Mary note how rare rules interact • I see a bird • is this 4 nouns – parsed like “city park scavenger bird”? rare parts of speech, plus systematic ambiguity in noun sequences • Time flies like an arrow • Fruit flies like a banana • Time reactions like this one • Time reactions like a chemist • or is it just an NP? 600.465 - Intro to NLP - J. Eisner

  3. Our bane: Ambiguity • John saw Mary • Typhoid Mary • Phillips screwdriver Mary note how rare rules interact • I see a bird • is this 4 nouns – parsed like “city park scavenger bird”? rare parts of speech, plus systematic ambiguity in noun sequences • Time | flies like an arrow NP VP • Fruit flies | like a banana NP VP • Time | reactions like this one V[stem] NP • Time reactions | like a chemist S PP • or is it just an NP? 600.465 - Intro to NLP - J. Eisner

  4. How to solve this combinatorial explosion of ambiguity? • First try parsing without any weird rules, throwing them in only if needed. • Better: every rule has a weight. A tree’s weight is total weight of all its rules. Pick the overall lightest parse of sentence. • Can we pick the weights automatically?We’ll get to this later … 600.465 - Intro to NLP - J. Eisner

  5. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  6. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  7. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  8. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  9. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  10. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  11. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  12. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  13. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  14. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  15. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  16. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  17. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  18. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  19. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  20. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  21. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  22. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  23. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  24. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  25. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  26. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  27. 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  28. S Follow backpointers … 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  29. S NP VP 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  30. S NP VP PP VP 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  31. S NP VP PP VP P NP 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  32. S NP VP PP VP P NP Det N 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  33. Which entries do we need? 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  34. Which entries do we need? 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  35. Not worth keeping … 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  36. … since it just breeds worse options 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  37. Keep only best-in-class! inferior stock 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  38. Keep only best-in-class! (and its backpointers so you can recover best parse) 1 S  NP VP 6 S  Vst NP 2 S  S PP 1 VP  V NP 2 VP  VP PP 1 NP  Det N 2 NP  NP PP 3 NP  NP NP 0 PP  P NP

  39. Chart Parsing phrase(X,I,J) :- rewrite(X,W), word(W,I,J). phrase(X,I,J) :- rewrite(X,Y,Z), phrase(Y,I,Mid), phrase(Z,Mid,J). goal :- phrase(start_symbol, 0, sentence_length). 39

  40. Weighted Chart Parsing (“min cost”) phrase(X,I,J) min= rewrite(X,W) + word(W,I,J). phrase(X,I,J) min= rewrite(X,Y,Z) + phrase(Y,I,Mid) + phrase(Z,Mid,J). goal min= phrase(start_symbol, 0, sentence_length). 40

  41. Probabilistic Trees • Instead of lightest weight tree, take highest probability tree • Given any tree, your assignment 1 generator would have some probability of producing it! • Just like using n-grams to choose among strings … • What is the probability of this tree? S NP time VP PP VP flies P like NP Det an N arrow 600.465 - Intro to NLP - J. Eisner

  42. Probabilistic Trees • Instead of lightest weight tree, take highest probability tree • Given any tree, your assignment 1 generator would have some probability of producing it! • Just like using n-grams to choose among strings … • What is the probability of this tree? • You rolled a lot of independent dice … S NP time VP | S) p( PP VP flies P like NP Det an N arrow 600.465 - Intro to NLP - J. Eisner

  43. Chain rule: One word at a time p(time flies like an arrow) = p(time) * p(flies | time) * p(like | time flies) * p(an | time flies like) * p(arrow | time flies like an) 600.465 - Intro to NLP - J. Eisner

  44. Chain rule + backoff (to get trigram model) p(time flies like an arrow) = p(time) * p(flies | time) * p(like | time flies) * p(an | time flies like) * p(arrow | time flies like an) 600.465 - Intro to NLP - J. Eisner

  45. Chain rule – written differently p(time flies like an arrow) = p(time) * p(time flies | time) * p(time flies like | time flies) * p(time flies like an | time flies like) * p(time flies like an arrow | time flies like an) Proof: p(x,y | x) = p(x | x) * p(y | x, x) = 1 * p(y | x) 600.465 - Intro to NLP - J. Eisner

  46. Chain rule + backoff p(time flies like an arrow) = p(time) * p(time flies | time) * p(time flies like | time flies) * p(time flies like an | time flies like) * p(time flies like an arrow | time flies like an) Proof: p(x,y | x) = p(x | x) * p(y | x, x) = 1 * p(y | x) 600.465 - Intro to NLP - J. Eisner

  47. Chain rule: One node at a time S S S S NP time VP | S) * p( | ) | S) = p( p( NP NP time VP VP NP VP PP VP flies P like NP S S * p( | ) Det an N arrow NP time NP time VP VP PP VP S S * p( | ) * … NP time NP time VP VP PP VP flies PP VP 600.465 - Intro to NLP - J. Eisner

  48. model you used in homework 1! (called “PCFG”) Chain rule + backoff S S S S NP time VP | S) * p( | ) | S) = p( p( NP NP time VP VP NP VP PP VP flies P like NP S S * p( | ) Det an N arrow NP time NP time VP VP PP VP S S * p( | ) * … NP time NP time VP VP PP VP flies PP VP 600.465 - Intro to NLP - J. Eisner

  49. model you used in homework 1! (called “PCFG”) Simplified notation S NP time VP | S) = p(S NP VP| S) * p(NP  time| NP) p( PP VP flies P like NP * p(VP VP NP| VP) Det an N arrow * p(VP flies| VP) * … 600.465 - Intro to NLP - J. Eisner

  50. Already have a CKY alg for weights … S NP time VP | S) = w(S NP VP) + w(NP  time) w( PP VP flies P like NP + w(VP VP NP) Det an N arrow + w(VP flies) + … Just let w(X Y Z) = -log p(X Y Z| X) Then lightest tree has highest prob

More Related