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CS626-449: Speech, NLP and the Web/Topics in AI

CS626-449: Speech, NLP and the Web/Topics in AI. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 11: Evidence for Deeper Structure; Top Down Parsing Algorithm. How deep should a tree be?. Is there a principle in branching When should the constituent give rise to children?

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CS626-449: Speech, NLP and the Web/Topics in AI

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  1. CS626-449: Speech, NLP and the Web/Topics in AI Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 11: Evidence for Deeper Structure; Top Down Parsing Algorithm

  2. How deep should a tree be? • Is there a principle in branching • When should the constituent give rise to children? • What is the hierarchy building principle?

  3. 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.

  4. Other languages English NP PP The AP PP book with the blue cover big of poems NP Hindi AP PP PP kitaab kavita kii niil jilda vaalii badii [niil jilda vaalii kavita kii kitaab]

  5. Other languages: contd English NP PP The AP PP book with the blue cover big of poems NP Bengali AP PP PP ti bai kavitar niil malaat deovaa motaa [niil malaat deovaa kavitar bai ti]

  6. 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.

  7. “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

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

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

  10. Parsing Algorithm

  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).

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