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CS626-449: Speech, Natural Language Processing and the Web/Topics in Artificial Intelligence

CS626-449: Speech, Natural Language Processing and the Web/Topics in Artificial Intelligence. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 12: Deeper Verb Structure; Different Parsing Algorithms. Types of Languages:-. Head Initial Indian Languages Japanese (with a bit of exception)

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CS626-449: Speech, Natural Language Processing and the Web/Topics in Artificial Intelligence

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  1. CS626-449: Speech, Natural Language Processing and the Web/Topics in Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 12: Deeper Verb Structure; Different Parsing Algorithms

  2. Types of Languages:- • Head Initial • Indian Languages • Japanese (with a bit of exception) • Head Final • English

  3. Head Initial & Head Final

  4. Deeper trees needed for capturing sentence structure This wont do! Flat structure! 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.

  5. “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

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

  7. To target N1’ • I want [NPthis [N’big book of poems with the red cover] and not [Nthat [None]]

  8. V-bar • What is the element in verbs corresponding to one-replacement for nouns • do-so or did-so

  9. I [eat beans with a fork] VP PP eat NP with a fork beans No constituent that groups together V and NP and excludes PP

  10. Need for intermediate constituents • I [eat beans] with a fork but Ram [does so] with a spoon VP V1’ VPV’ V’ V’ (PP) V’ V (NP) V2’ PP V NP with a fork eat beans

  11. How to target V1’ • I [eat beans with a fork], and Ram [does so] too. VP V1’ VPV’ V’ V’ (PP) V’ V (NP) V2’ PP V NP with a fork eat beans

  12. Parsing Algorithm

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

  14. A segment of English Grammar • S’(C) S • S{NP/S’} VP • VP(AP+) (VAUX) V (AP+) ({NP/S’}) (AP+) (PP+) (AP+) • NP(D) (AP+) N (PP+) • PPP NP • AP(AP) A

  15. 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”

  16. 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

  17. 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).

  18. 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

  19. 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?

  20. 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) ……..

  21. Combining top-down and bottom-up strategies

  22. 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

  23. 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

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

  25. 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

  26. 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.

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

  28. 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”

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

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

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