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Pushpak Bhattacharyya CSE Dept., IIT Bombay 8 th , 10 th March , 2011

CS460/626 : Natural Language Processing/Speech, NLP and the Web (Lecture 23, 24– Parsing Algorithms; Parsing in case of Ambiguity; Probabilistic Parsing). Pushpak Bhattacharyya CSE Dept., IIT Bombay 8 th , 10 th March , 2011

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Pushpak Bhattacharyya CSE Dept., IIT Bombay 8 th , 10 th March , 2011

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  1. CS460/626 : Natural Language Processing/Speech, NLP and the Web(Lecture 23, 24– Parsing Algorithms; Parsing in case of Ambiguity; Probabilistic Parsing) Pushpak BhattacharyyaCSE Dept., IIT Bombay 8th, 10th March, 2011 (Lectures 21 and 22 were on Sentiment Analysis by Aditya Joshi)

  2. A note on Language Modeling • Example sentence • “ ^ The tortoise beat the hare in the race.” Guided Guidedby Guided by by frequency Language world KnowledgeKnowledge

  3. Parse Tree S NP VP DT N V NP PP The Tortoise beatDT N P NP the hare in DT N the race

  4. UNL Expression beat@past scn(scene) agt obj race@def tortoise@def hare@def

  5. Purpose of LM • Prediction of next word (Speech Processing) • Language Identification (for same script) • Belongingness check (parsing) • P(NP->DT N) means what is the probability that the ‘YIELD’ of the non terminal NP is DT N

  6. Need for Deep Parsing • Sentences are linear structures • But there is a hierarchy- a tree- hidden behind the linear structure • There are constituents and branches

  7. PPs are at the same level: flat with respect to the head word “book” No distinction in terms of dominance or c-command NP PP The AP PP book with the blue cover of poems big [The big book of poems with the Blue cover] is on the table.

  8. “Constituency test of Replacement” runs into problems • One-replacement: • I bought the big [book of poems with the blue cover] not the small [one] • One-replacement targets book of poems with the blue cover • Another one-replacement: • I bought the big [book of poems] with the blue cover not the small [one] with the red cover • One-replacement targets book of poems

  9. More deeply embedded structure NP N’1 The AP N’2 N’3 big PP N book with the blue cover PP of poems

  10. Grammar and Parsing Algorithms

  11. A simplified grammar • S  NP VP • NP  DT N | N • VP  V ADV | V

  12. Example Sentence People laugh • 2 3 Lexicon: People - N, V Laugh - N, V These are positions This indicate that both Noun and Verb is possible for the word “People”

  13. Top-Down Parsing State Backup State Action ----------------------------------------------------------------------------------------------------- 1. ((S) 1) - - 2. ((NP VP)1) - - 3a. ((DT N VP)1) ((N VP) 1) - 3b. ((N VP)1) - - 4. ((VP)2) - Consume “People” 5a. ((V ADV)2) ((V)2) - 6. ((ADV)3) ((V)2) Consume “laugh” 5b. ((V)2) - - 6. ((.)3) - Consume “laugh” Termination Condition : All inputs over. No symbols remaining. Note: Input symbols can be pushed back. Position of input pointer

  14. Discussion for Top-Down Parsing • This kind of searching is goal driven. • Gives importance to textual precedence (rule precedence). • No regard for data, a priori (useless expansions made).

  15. Bottom-Up Parsing Some conventions: N12 S1? -> NP12 ° VP2? Represents positions Work on the LHS done, while the work on RHS remaining End position unknown

  16. Bottom-Up Parsing (pictorial representation) S -> NP12 VP23° People Laugh 1 2 3 N12 N23 V12 V23 NP12 -> N12 ° NP23 -> N23 ° VP12 -> V12 ° VP23 -> V23 ° S1? -> NP12° VP2?

  17. Problem with Top-Down Parsing • Left Recursion • Suppose you have A-> AB rule. Then we will have the expansion as follows: • ((A)K) -> ((AB)K) -> ((ABB)K) ……..

  18. Combining top-down and bottom-up strategies

  19. Top-Down Bottom-Up Chart Parsing • Combines advantages of top-down & bottom-up parsing. • Does not work in case of left recursion. • e.g. – “People laugh” • People – noun, verb • Laugh – noun, verb • Grammar – S  NP VP NP  DT N | N VP  V ADV | V

  20. Transitive Closure People laugh 1 2 3 S NP VP NP N VP  V  NP DT N S  NPVP S  NP VP  NP N VP V ADV success VP V

  21. Arcs in Parsing • Each arc represents a chart which records • Completed work (left of ) • Expected work (right of )

  22. Example People laugh loudly 1 2 3 4 S  NP VP NP  N VP  V VP  V ADV NP  DT N S  NPVP VP  VADV S  NP VP NP  N VP V ADV S  NP VP VP V

  23. Dealing With Structural Ambiguity • Multiple parses for a sentence • The man saw the boy with a telescope. • The man saw the mountain with a telescope. • The man saw the boy with the ponytail. At the level of syntax, all these sentences are ambiguous. But semantics can disambiguate 2nd & 3rd sentence.

  24. Prepositional Phrase (PP) Attachment Problem V – NP1 – P – NP2 (Here P means preposition) NP2 attaches to NP1 ? or NP2 attaches to V ?

  25. Parse Trees for a Structurally Ambiguous Sentence Let the grammar be – S  NP VP NP  DT N | DT N PP PP  P NP VP  V NP PP | V NP For the sentence, “I saw a boy with a telescope”

  26. Parse Tree - 1 S NP VP N V NP Det N PP saw I a P NP boy Det N with a telescope

  27. Parse Tree -2 S NP VP PP N V NP P NP Det N saw I Det N with a boy a telescope

  28. Parsing Structural Ambiguity

  29. Parsing for Structurally Ambiguous Sentences Sentence “I saw a boy with a telescope” Grammar: S  NP VP NP  ART N | ART N PP | PRON VP  V NP PP | V NP ART  a | an | the N  boy | telescope PRON  I V  saw

  30. Ambiguous Parses • Two possible parses: • PP attached with Verb (i.e. I used a telescope to see) • ( S ( NP ( PRON “I” ) ) ( VP ( V “saw” ) ( NP ( (ART “a”) ( N “boy”)) ( PP (P “with”) (NP ( ART “a” ) ( N “telescope”))))) • PP attached with Noun (i.e. boy had a telescope) • ( S ( NP ( PRON “I” ) ) ( VP ( V “saw” ) ( NP ( (ART “a”) ( N “boy”) (PP (P “with”) (NP ( ART “a” ) ( N “telescope”))))))

  31. Top Down Parse

  32. Top Down Parse

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  45. Top Down Parse

  46. Top Down Parsing - Observations Top down parsing gave us the Verb Attachment Parse Tree (i.e., I used a telescope) To obtain the alternate parse tree, the backup state in step 5 will have to be invoked Is there an efficient way to obtain all parses ?

  47. Bottom Up Parse I saw a boy with a telescope 1 2 3 4 5 6 7 8 Colour Scheme : Blue for Normal Parse Green for Verb Attachment Parse Purplefor Noun Attachment Parse Redfor Invalid Parse

  48. Bottom Up Parse NP12  PRON12 S1?NP12VP2? I saw a boy with a telescope 1 2 3 4 5 6 7 8

  49. Bottom Up Parse NP12  PRON12 VP2?V23NP3? S1?NP12VP2? VP2?V23NP3?PP?? I saw a boy with a telescope 1 2 3 4 5 6 7 8

  50. Bottom Up Parse NP12  PRON12 VP2?V23NP3? NP35 ART34N45 S1?NP12VP2? VP2?V23NP3?PP?? NP3?ART34N45PP5? I saw a boy with a telescope 1 2 3 4 5 6 7 8

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