1. CS626-449: Speech, NLP and the Web/Topics in AI Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 13: Deeper Adjective and PP Structure; Structural Ambiguity and Parsing

2. Types of Grammar Prescriptive Grammar Taught in schools Emphasis is on usage Descriptive Grammar Also known as Linguistic Grammar Describes Language

3. Types of Languages SVO Subject – Verb – Object English E.g. Ram likes music. S V O SOV Subject- Object-Verb Indian Languages E.g. राम पानी पी रहा है | S O V

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

5. Bar-level projections Add intermediate structures NP  (D) N’ N’  (AP) N’ | N’ (PP) | N (PP) () indicates optionality

6. New rules produce this tree NP PP AP big The of poems with the blue cover N’ 1 N book PP N’ 2 N’ 3 N-bar

7. As opposed to this tree NP PPAP big The of poems with the blue coverbook PP

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. Case of conjunction V3’V3’ NP beans eat In the afternoon PP VP V1’V1’ V V4’V4’ NP coffee drink V V2’V2’ Conj and

13. A-bar: adjectives A3’A3’ A4’A4’ blue Very AP bright A5’A5’ A6’A6’ green A1’A1’ AP A2’A2’ Conj and AP dull AP  A’ A’  (AP) A’ A’  A (PP)

14. So-replacement for adjectives Ram is very serious about studies, but less so than Shyam

15. P-bar: prepositions P2’P2’ NP the table right AP off P3’P3’ NP the trash A1’A1’ AP P1’P1’ Conj and P P into PP  P’ P’  P’ (PP) P’  P (NP) PP

16. So-replacement for Prepositions Ram is utterly in debt, but Shyam is only partly so.

17. Complements and Adjuncts or Arguments and Adjuncts

18. Rules in bar notation: Noun NP  (D) N’ N’  (AP) N’ N’  N’ (PP) N’  N (PP)

19. Rules in bar notation: Verb VP  V’ V’  V’ (PP) V’  V (NP)

20. Rules in bar notation: Adjective AP  A’ A’  (AP) A’ A’  A (PP)

21. Rules in bar notation: Preposition PP  P’ P’  P’ (PP) P’  P (NP)

22. Introducing the “X factor” Let X stand for any category N, V, A, P Let XP stand for NP, VP, AP and PP Let X’ stand for N’, V’, A’ and P’

23. XP to X’ Collect the first level rules NP  (D) N’ VP  V’ AP  A’ PP  P’ And produce XP  (YP) X’

24. X’ to X’ Collect the 2 nd level rules N’  (AP) N’ or N’ (PP) V’  V’ (PP) A’  (AP) A’ P’  P’ (PP) And produce X’  (ZP) X’ or X (ZP)

25. X’ to X Collect the 3 rd level rules N’  N (PP) V’  V (NP) A’  A (PP) P’  P (NP) And produce X’  X (WP)

26. Basic observations about X and X’ X’  X (WP) X’  X’ (ZP) X is called Head Phrases must have Heads: Headedness property Category of XP and X must match: Endocentricity

27. Basic observations about X and X’ X’  X (WP) X’  X’ (ZP) Sisters of X are complements Roughly correspond to objects Sisters of X’ are Adjuncts PPs and Adjectives are typical adjuncts We have adjunct rules and complement rules

28. Structural difference between complements and adjuncts X’ WP Complement X’ X ZP XP Adjunct

29. Complements and Adjuncts in NPs N’ PP of poems N’ N ZP NP with red cover book

30. Any number of Adjuncts N’ PP of poems N’ N ZP N’ with red cover book NP from Oxford Press

31. Parsing Algorithm

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

33. 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

34. 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”

35. 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

36. 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).

37. 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

38. 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?

39. 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) ……..

40. Combining top-down and bottom-up strategies

41. 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

42. Transitive Closure People laugh 123 S  NP VPNP  N  VP  V  NP  DT NS  NP  VPS  NP VP  NP  NVP  V ADVsuccess VP  V

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

44. 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

45. 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.

46. 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 ?

47. 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”

48. Parse Tree - 1 S NPVP NVNP DetNPP PNP DetN I saw a boy with atelescope

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

50. Parsing Structural Ambiguity

51. Topics to be covered Dealing with Structural Ambiguity Moving towards Dependency Parsing

52. 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

53. 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”))))))

54. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP

55. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON

56. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used

57. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used 3B3B ( ( PRON VP ) 1 )− −

58. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used 3B3B ( ( PRON VP ) 1 )− − 4( ( VP ) 2 )− Consumed “I”

59. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used 3B3B ( ( PRON VP ) 1 )− − 4( ( VP ) 2 )− Consumed “I” 5( ( V NP PP ) 2 )( ( V NP ) 2 ) −Verb Attachment Rule used

60. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used 3B3B ( ( PRON VP ) 1 )− − 4( ( VP ) 2 )− Consumed “I” 5( ( V NP PP ) 2 )( ( V NP ) 2 ) −Verb Attachment Rule used 6( ( NP PP ) 3 )− Consumed “saw”

61. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used 3B3B ( ( PRON VP ) 1 )− − 4( ( VP ) 2 )− Consumed “I” 5( ( V NP PP ) 2 )( ( V NP ) 2 ) −Verb Attachment Rule used 6( ( NP PP ) 3 )− Consumed “saw” 7( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 ) (b) ( ( PRON PP ) 3 )

62. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used 3B3B ( ( PRON VP ) 1 )− − 4( ( VP ) 2 )− Consumed “I” 5( ( V NP PP ) 2 )( ( V NP ) 2 ) −Verb Attachment Rule used 6( ( NP PP ) 3 )− Consumed “saw” 7( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 ) (b) ( ( PRON PP ) 3 ) 8( ( N PP) 4 )− Consumed “a”

63. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used 3B3B ( ( PRON VP ) 1 )− − 4( ( VP ) 2 )− Consumed “I” 5( ( V NP PP ) 2 )( ( V NP ) 2 ) −Verb Attachment Rule used 6( ( NP PP ) 3 )− Consumed “saw” 7( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 ) (b) ( ( PRON PP ) 3 ) 8( ( N PP) 4 )− Consumed “a” 9( ( PP ) 5 )− Consumed “boy”

64. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP 2( ( NP VP ) 1 )− −Use NP  ART N | ART N PP | PRON 3( ( ART N VP ) 1 ) (a) ( ( ART N PP VP ) 1 ) (b) ( ( PRON VP ) 1) −ART does not match “I”, backup state (b) used 3B3B ( ( PRON VP ) 1 )− − 4( ( VP ) 2 )− Consumed “I” 5( ( V NP PP ) 2 )( ( V NP ) 2 ) −Verb Attachment Rule used 6( ( NP PP ) 3 )− Consumed “saw” 7( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 ) (b) ( ( PRON PP ) 3 ) 8( ( N PP) 4 )− Consumed “a” 9( ( PP ) 5 )− Consumed “boy” 10 ( ( P NP ) )− −

65. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP ……… …… 7( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 ) (b) ( ( PRON PP ) 3 ) 8( ( N PP) 4 )− Consumed “a” 9( ( PP ) 5 )− Consumed “boy” 10( ( P NP ) 5 )− − 11 ( ( NP ) 6 )− Consumed “with”

66. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP ……… …… 7( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 ) (b) ( ( PRON PP ) 3 ) 8( ( N PP) 4 )− Consumed “a” 9( ( PP ) 5 )− Consumed “boy” 10( ( P NP ) 5 )− − 11 ( ( NP ) 6 )− Consumed “with” 12( ( ART N ) 6 ) (a) ( ( ART N PP ) 6 ) (b) ( ( PRON ) 6) −

67. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP ……… …… 7( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 ) (b) ( ( PRON PP ) 3 ) 8( ( N PP) 4 )− Consumed “a” 9( ( PP ) 5 )− Consumed “boy” 10( ( P NP ) 5 )− − 11 ( ( NP ) 6 )− Consumed “with” 12( ( ART N ) 6 ) (a) ( ( ART N PP ) 6 ) (b) ( ( PRON ) 6) − 13( ( N ) 7 )− Consumed “a”

68. Top Down Parse StateBackup State ActionComments 1( ( S ) 1 )− −Use S  NP VP ……… …… 7( ( ART N PP ) 3 ) (a) ( ( ART N PP PP ) 3 ) (b) ( ( PRON PP ) 3 ) 8( ( N PP) 4 )− Consumed “a” 9( ( PP ) 5 )− Consumed “boy” 10( ( P NP ) 5 )− − 11 ( ( NP ) 6 )− Consumed “with” 12( ( ART N ) 6 ) (a) ( ( ART N PP ) 6 ) (b) ( ( PRON ) 6) − 13( ( N ) 7 )− Consumed “a” 14( ( − ) 8 )− Consume “telescope” Finish Parsing

69. 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 ?

70. 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 Purple for Noun Attachment Parse Red for Invalid Parse Bottom Up Parse

71. I saw a boy with a telescope 1 2 3 4 5 6 7 8 Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2?

72. I saw a boy with a telescope 1 2 3 4 5 6 7 8 Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3?

73. I saw a boy with a telescope 1 2 3 4 5 6 7 8 Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5?

74. I saw a boy with a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? NP 35  ART 34 N 45

75. I saw a boywith a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? NP 35  ART 34 N 45 VP 25  V 23 NP 35 S 15  NP 12 VP 25 VP 2?  V 23 NP 35 PP 5?

76. I saw a boy with a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? PP 5?  P 56 NP 6? NP 35  ART 34 N 45 VP 25  V 23 NP 35 S 15  NP 12 VP 25 VP 2?  V 23 NP 35 PP 5?

77. I saw a boywith a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? PP 5?  P 56 NP 6? NP 35  ART 34 N 45 NP 68  ART 67 N 7? NP6 ?  ART 67 N 78 PP 8? VP 25  V 23 NP 35 S 15  NP 12 VP 25 VP 2?  V 23 NP 35 PP 5?

78. I saw a boywith a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? PP 5?  P 56 NP 6? NP 35  ART 34 N 45 NP 68  ART 67 N 7? NP6 ?  ART 67 N 78 PP 8? NP 68  ART 67 N 78 VP 25  V 23 NP 35 S 15  NP 12 VP 25 VP 2?  V 23 NP 35 PP 5?

79. I saw a boy with a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? PP 5?  P 56 NP 6? NP 35  ART 34 N 45 NP 68  ART 67 N 7? NP6 ?  ART 67 N 78 PP 8? NP 68  ART 67 N 78 VP 25  V 23 NP 35 S 15  NP 12 VP 25 VP 2?  V 23 NP 35 PP 5? PP 58  P 56 NP 68

80. I saw a boy with a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? PP 5?  P 56 NP 6? NP 35  ART 34 N 45 NP 68  ART 67 N 7? NP6 ?  ART 67 N 78 PP 8? NP 68  ART 67 N 78 VP 25  V 23 NP 35 S 15  NP 12 VP 25 VP 2?  V 23 NP 35 PP 5? PP 58  P 56 NP 68 NP 38  ART 34 N 45 PP 58

81. I saw a boywith a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? PP 5?  P 56 NP 6? NP 35  ART 34 N 45 NP 68  ART 67 N 7? NP6 ?  ART 67 N 78 PP 8? NP 68  ART 67 N 78 VP 25  V 23 NP 35 S 15  NP 12 VP 25 VP 2?  V 23 NP 35 PP 5? PP 58  P 56 NP 68 NP 38  ART 34 N 45 PP 58 VP 28  V 23 NP 38 VP 28  V 23 NP 35 PP 58

82. I saw a boy with a telescope 1 2 3 4 5 6 7 8 NP 3?  ART 34 N 45 PP 5? Bottom Up Parse NP 12  PRON 12 S 1?  NP 12 VP 2? VP 2?  V 23 NP 3? PP ?? VP 2?  V 23 NP 3? NP 35  ART 34 N 45 NP 3?  ART 34 N 45 PP 5? PP 5?  P 56 NP 6? NP 35  ART 34 N 45 NP 68  ART 67 N 7? NP6 ?  ART 67 N 78 PP 8? NP 68  ART 67 N 78 VP 25  V 23 NP 35 S 15  NP 12 VP 25 VP 2?  V 23 NP 35 PP 5? PP 58  P 56 NP 68 NP 38  ART 34 N 45 PP 58 VP 28  V 23 NP 38 VP 28  V 23 NP 35 PP 58 S 18  NP 12 VP 28

83. Bottom Up Parsing - Observations Both Noun Attachment and Verb Attachment Parses obtained by simply systematically applying the rules Numbers in subscript help in verifying the parse and getting chunks from the parse

84. Exercise For the sentence, “The man saw the boy with a telescope” & the grammar given previously, compare the performance of top-down, bottom-up & top-down chart parsing.

85. Verb Alternation (1/2) (ref: Natural Language Understanding, James Allan) VerbComplement Structure Example laughEmpty (in transitive)Ram laughed findNP (transitive)Ram found the key giveNP+NP (di transitive)Ram gave Sita the paper giveNP+PP [to]Ram gave the paper to Sita ResideLoc PhraseRam resides in Mumbai putNP+loc phraseRam put the book inside the box speakPP [with]+PP[about]Ram with Sita about floods tryVP[to]Ram tried to apologise tellNP+VP[to]Ram told the man to go

86. Verb Alternation (1/2) VerbComplement Structure Example wishS [to]Ram wished for the man to go keepVP [ing]Ram keeps hoping for the best catchNP+VP [ing]Ram caught Shyam looking in his desk WatchNP+VP [base]Ram watched Shyam eat the pizza regretS [that]Ram regretted that he had eaten the whole thing TellNP+S [that]Ram told Sita that he was sorry SeemADJPRam seems unhappy in his new job ThinkNP+ADJPRam thinks Sita is happy KnowS [wh]Ram knows where to go