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 HEAD INITIALHEAD FINAL NPeats rice चावल खा रहा है VPpresident of India भारत के राष्ट्रपति PPof India भारत के ADJPvery intelligent बहुत बुद्धिमान

4. Deeper trees needed for capturing sentence structure NP PPAP big The of poems with the blue cover [The big book of poems with the Blue cover] is on the table. book This wont do! Flat structure! PP

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 PP AP big The of poems with the blue cover N’ 1 N book PP N’ 2 N’ 3

7. To target N 1 ’ I want [ NP this [ N’ big book of poems with the red cover] and not [ N that [ N one]]

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 NP beans eat with a fork PP 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 V2’V2’ NP beans eat with a fork PP VP V1’V1’ V VP  V’ V’  V’ (PP) V’  V (NP)

11. How to target V 1 ’ I [eat beans with a fork], and Ram [does so] too. V2’V2’ NP beans eat with a fork PP VP V1’V1’ V VP  V’ V’  V’ (PP) V’  V (NP)

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 1 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: N 12 S 1? -> NP 12 ° VP 2? Represents positions End position unknown Work on the LHS done, while the work on RHS remaining

19. Bottom-Up Parsing (pictorial representation) S -> NP 12 VP 23 ° People Laugh 1 2 3 N 12 N 23 V 12 V 23 NP 12 -> N 12 ° NP 23 -> N 23 ° VP 12 -> V 12 ° VP 23 -> V 23 ° S 1? -> NP 12 ° VP 2?

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 123 S  NP VPNP  N  VP  V  NP  DT NS  NP  VPS  NP VP  NP  NVP  V ADVsuccess VP  V

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

25. Example People laughloudly 1234 S  NP VPNP  N  VP  V  VP  V ADV  NP  DT NS  NP  VPVP  V  ADVS  NP VP  NP  NVP   V ADVS  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 2 nd & 3 rd sentence.

27. Prepositional Phrase (PP) Attachment Problem V – NP 1 – P – NP 2 (Here P means preposition) NP 2 attaches to NP 1 ? or NP 2 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 NPVP NVNP DetNPP PNP DetN I saw a boy with atelescope

30. Parse Tree -2 S NPVP NVNP DetN PP PNP DetN I saw a boy with atelescope