1. Computational Paninian Grammar for Dependency Parsing Dipti Misra Sharma LTRC, IIIT, Hyderabad NLP Winter School 25-12-2008

2. Outline  Backgrond  Paninian Grammar :The Basic Framework  Some Example Cases  Conclusion

3. Background Indian languages  Rich morphology  Relatively flexible word order For example, 1. a) baccaa phala khaataa hai ‘child’ ‘fruit’ ‘eat+hab’ ‘pres’ b) phala baccaa khaataa hai c) phala khaataa hai baccaa d) baccaa khaataa hai phala

4. Basic Structure in PS NP VP S N baccaa NPVP N V Aux haikhaataa phala 1 a) baccaa phala khaataa hai ‘child’ ‘fruit’ ‘eat+hab’ ‘pres’ Subject – baccaa ‘child’ Object - phala ‘fruit’

5. PS for 1(b)‏ 1 b) phala baccaa khaataa hai ‘fruit’ ‘child’ ‘eat’ ‘pres’ Topic – phala ‘fruit’ Subject - baccaa ‘child’ Object - t Movement involved Tree - I

6. Problems Complex tree In what ways subject (baccaa) is different from object (phala) ?  Agreement does not hold  Position does not hold

7. How to Draw PSs for 1 (c-d) ? 1 c) baccaa khaata hai phala 'child' 'eat+hab' 'pres' 'fruit' 1 d) phala khaata hai baccaa 'fruit' 'eat+hab' 'pres' 'child' Simple and perfectly natural sentences - difficult to handle in Phrase Structure Dependency structures make it easy

8. Dependency Structure khaataa_ hai phala baccaa baccaa phala khaataa hai ‘child’ ‘fruit’ ‘eat’ ‘is’ phala baccaa khaataa hai ‘fruit’ ‘child’ ‘eat’ ‘is’ baccaa khaata hai phala ‘child’ ‘eat’ ‘is’ ‘fruit’ phala khaata hai baccaa ‘fruit’ ‘eat’ ‘is’ ‘child’ k1 k2 One dependency for all (1a-d)‏ Additional attribute of 'order' can be included to capture the variation in order Case and postpositions be encoded in role

9. Paninian Grammatical Formalism A dependency grammar based approach Motivation for following the Paninian approach  Inspired by inflectionally rich language (Sanskrit) ‏  Better suited for handling ILs  Provides the level of syntactico-semantic interface for parsing  Various linguistic phenomena handled seamlessly ( Refer Akshar Bharati et al Natural Language Parsing - a Paninian Perspective (1995) http://ltrc.iiit.net/showfile.php?filename=downloads/nlpb ook/index.html)

10. Panian Grammar Contd. The grammar facilitates realisation of the intended meaning as an 'expression' of what the speaker wants to communicate (vivaksha)‏

11. The Basic Framework Treats a sentence as a series of modifier- modified relations  A sentence has a primary modified (generally a verb) ‏ Provides a blueprint to identify these relations Syntactic cues help in identifying the relation types

12. Levels of Representation (1) Semantic information Assignment of karakas (Th-roles) and of abstract tense (2) Morphosyntactic representation Morphological spellout rules (3) Abstract morphological representation Allomorphy and phonology (4) Phonological output form (From Kiparsky, Lectures in CIEFL, Hyderabad, pg2) ‏

13. Some Concepts  Speaker's intention (vivakshaa) ‏  Root + Suffix (prakriti + pratyaya) ‏  Expectancy (aakaankshaa) ‏  Eligibility (yogyataa) ‏  Proximity (sannidhi) ‏  Karaka  vibhakti

14. Speaker’s Intention (vivakshaa)‏ Each sentence reflects speaker’s intention  Various sub-actions come into focus  Participants are assigned various relations accordingly  ‘key’ gets assigned karta, karana based on the kind of sub-action under focus Syntax reflects vivaksha

15. Prakriti and Pratyaya (root and suffix)‏ The premise Every word is composed of two parts 1. Content part (root morpheme) ‏ 2. Functional part (affix) ‏ For languages such as English and Hindi the auxiliaries can be treated as the functional morphemes Morph analysers or Local word groupers can provide this information

16. aakaankshaa (Expectation/Demand) ‏ Every word has certain demands to be fulfilled. For Parsing, verb is the most critical element The demand frames (karaka frames) for the verbs list out their demands

17.  For Example, frame of Hindi verb 'khaa' Verb  khaa Sense  to eat Sense ID ??? Eg  raam seb khaataa hai ‘Ram ate an apple’ ---------------------------------------------------------------------------------- arc-label necessity vibhakti lextype reln ---------------------------------------------------------------------------------- k1 m 0 n c k2 m 0 n c ----------------------------------------------------------------------------------------- k1  karta; k2  karma; m  mandatory; n  noun; c  child

18. Yogyataa (Eligibility)‏ Selectional Restrictions For example, baccaa phala khaataa hai 'phala' (fruit) does not have the eligibility to become the 'karta' of the verb 'khaa' (eat) ‏ Constraints based on yogyata require semantic knowledge for each lexical item This knowledge can be obtained from a lexical resource such as a 'WordNet'

19. Sannidhi (Proximity)‏ The modifier and the modified tend to occur in close proximity in a sentence For example, ' rAma ne kelaa khaayaa, mohana ne duudha piyaa Ora Hari ne film dekhii' This Hindi example cotains three verbs - khAyA (ate), piyA (drank) and dekhI (saw) ‏ Respective arguments of each of these verbs would tend to occur in close proximity to it

20. Karaka and Vibhakti Two levels of analysis  Syntactico-sematic relations :  Direct participants of the action denoted by a verb (Karaka) ‏  Other relations : purpose, genitive, reason etc  Relation markers (Vibhaktis) ‏

21. Semantics of the verb A verbal root denotes:  The activity  The result Locus of activity : karta Locus of result : karma Verbal Root activityresult

22. karta - karma The boy opened the lock  k1 – karta  k2 – karma karta, karma sometimes correspond to agent/theme  Not always open boylock k1k2

23. Action – bundle of sub-actions The boy opened the lock with the key The key opened the lock The lock opened Notion of vivaksha  Realization of speakers’ intention in a sentence

24. Sub-actions - Opening of lock

25. Action 1  The boy opened the lock with the key Action 2  The key opened the lock Action 3  The lock opened Each sentence reflects speakers’ intention

26. Sub-actions - Opening of lock open boy lockkey k1 k2 k3 open lock key k1 k2 k1 – karta (doer)‏ k2 – karma (affected)‏ k3 – karana (instrument)‏

27. Basic karaka relations Only six  karta – subject/agent/doer  karma – object/patient  karana – instrument  sampradaan – beneficiary  apaadaan – source  adhikarana – location in place/time/other

28. Basic karaka relations raama phala khaataa hai ‘Ram eats fruits’

29. Basic karaka relations raama chaaku se seba kaaTtaa hai ‘Ram cuts the apple with knife’

30. Basic karaka relations raama ne mohana ko pustaka dii ‘Ram gave a book to Mohan’

31. Other relations Other dependency relations  Purpose, reason, direction etc  Causatives, associatives, comparatives etc  Genitive, adjective

32. Vibhaktis : Markers for karaka Relations Relation markers (Vibhaktis ) ‏ raama ne caakuu se seba kaaTaa 'Ram‘ 'erg' 'knife‘ 'with' 'apple' 'cut' | | | karta(doer) karana(instrument) karma (theme) ‏ raama ne mohana ke_liye seba kaaTaa ‘ Ram’ ‘erg’ ‘Mohan’ ‘for’ ‘apple’ ‘cut’ “Ram cut the apple for Mohan” (purpose) ‏ maiM mohana ke_saatha baazaara gayaa ‘I’ ‘Mohan’ ‘with’ ‘market’ ‘went’ “ I went to the market with Mohan “ (associative )

33. However No one-to-one correspondence between relations and relation markers

34. Syntactic Cues Verbal inflections (Tense Aspect Modality (TAM)) ‏  Passive : verb agrees with the karma  Some other cases raama ko jaanaa paDaa ‘I+to’ ‘go’ ‘had to’ “I had to go” raama ko calanaa caahiye ‘Ram’ ‘to’ ‘walk’ ‘should’ “I should leave”

35. Example Raama jaataa hai ‘Ram’ ‘go+hab’ ‘pres’ “Ram goes” jaa karta raama Raama ko jaanaa paDaa ‘Ram+to’ ‘go’ ‘had to’ “ Ram had to go” jaa karta mujha

36. Some Examples Relative Clause MWEs Change of state verbs Conjuncts Ellipsis

37. Relative Clause A noun is modified by a clause with a relative pronoun as its co- referent Example meraa bhaaii jo dillii meM rahataa hai kala aa ‘my’ ‘brother’ ‘who’ ‘Delhi’ ‘in’ ‘live+hab’ ‘pres’ ‘tomorrow’ ‘come’ rahaa hai ‘prog’ ‘pres’ ‘My brother who lives in Delhi is coming tomorrow’ How to represent this ? Two possible representations

38. Alternative 1 aa meraa bhaaiikala jo raha dillii

39. Alternative 2 Aa meraa bhaaii kala coref raha jo dillii

40. Other Relative-Corelative Constructions Adjective having a clausal modifier tuma aisaa sundara ghar banaao jaisaa unakaa hai ‘you’ ‘such’ ‘beautiful’ ‘house’ ‘build’ ‘such-that’ ‘theirs’ ‘is’ “You build a house as beautiful as theirs” banaao ‘build’ k1 k2 tuma ghara adj sundara jjmod aisaa coref jo-vo-jjmod hai jaisaa unakaa jaisaa usakaa

41. MWEs  Conjunct Verbs ((raama ne)) ((bahuta dera)) ((ravi kii)) ((pratiikshaa kii)) ‏ 'rAma erg' 'very' 'late' 'ravi' ‘of' 'wait‘ ‘did’ Ram waited for Ravi for a long time ((kaaryashaalaa ke liye)) ((biisa logoM kaa)) ((naamaaMkana kiyaa gayaa)) ‏ 'workshop‘ 'for' 'twenty' 'people' ‘of‘ 'name registration' 'do+passive‘ Twenty people were registered for the workshop

42. Conjunct Verbs  Conjunct verb ‘prashna kiyaa’ below mohana ne ravi se prashna kiyaa ' Mohan' 'erg' 'Ravi' 'to' 'question' 'did' “Mohan asked Ravi a question”  A conjunct verb can have partial modification mohana ne acchaa prashna kiyaa thaa 'Mohan' 'erg' 'good' 'question' 'do+perf' 'past‘  The elements in a complex predicate can also be dis- continuous prashna to mohana ne kiyaa thaa 'question' 'part' 'Mohan' 'erg' 'do+perf' 'past'

43. Conjunct Verbs However, Mohan ne ravi se acchaa prashna kiyaa prashna_kiyaa ‘questioned’ k1 k2 ? mohan ne ravi se acchaa Mohan to Ravi good 'acchaa' is NOT a verb modifier, 'acchaa' modifies 'prashna' and not 'prashna kiyA', Solution ?

44. Conjunct Verbs Solution  Don't chunk a conjunct verb as a single verbal unit Thus, Mohan ne ravi se ((acchaa)) ((prashna kiyaa))_VG Revise to Mohan ne ravi se ((acchaa prashna))_NP ((kiyaa))_VG

45. Conjunct Verbs  Show 'part-of' relation between the noun and the verb  Add a tag 'pof' to achieve the above Therefore, _kiyaa k1 k2 pof mohan ne ravi se prashna nmod acchaa

46. DS for Discontinuous Elements prashna to mohana ne kiyaa thaa Use of pof ( ‘Part Of’ relation )‏ kiyaa mohanaprashna pof k1

47. MWEs  Idioms ((kisaana kii)) ((patnii ko)) ((vaha ciDiyaa)) ‏ 'farmer' 'of' 'wife' 'to' 'that' 'bird' (( phuuTii aaMkha nahiiM suhaatii thii)) ‏ 'not appealed' The idiom (in bold) is functionally a verb.

48. Idioms Two possible solutions phuuTii aazkha suhaa ‘not appealed’ k1 k2 patnii vaha ciDiyaa ‘wife ’ ‘that bird’ r6 kisaana ‘farmer’ Solution-1

49. Idioms suhaa ‘not appealed’ k2 pof k1 vaha ciDiyaa phuuTii aazkha patnii ‘that bird ’ ‘burst eye’ ‘wife’ r6 kisaana ‘farmer' Solution-2

50. Change of State Verbs Change of state verbs such as ‘raMganaa’ (colour) pose a problem such as, ((usane)) ((apanaa ghara)) ((piilaa)) ((raMgaa)) ‏ 'he/she' 'own' 'house' 'yellow' 'coloured' raMga ‘colour’ k1 k2 ? usane ghara piilaa he/she house yellow Is 'piilaa' a complement of 'ghara' ? OR Is it the k2 of raMgaa ? If ‘piilaa’ is the k2 of raMgaa then what is the relation of ‘ghara’ with ‘raMgaa ? Can they both be k2 ?

51. Proposed Solution In Panini's framework, verbs denoting 'change of state' can have two 'karma'  The object which is being changed  The state after change Thus, raMga ’coloured’ k1 k2-1 k2-2 usane ghara piilaa he house yellow

52. Conjuncts Need special treatment in a dependency representation (maiM baazaara gayaa)1 Ora (ve loga ghara para ruke)2 'I' 'market' 'went' 'and' 'those' people‘ 'home' 'at‘ 'stayed' “I went to the market and those people stayed at home” What is the head of a co-ordinate structure ? How to represent the equal status of 1 and 2 above ?

53. Conjuncts  Take Conjunct as the 'head'  Label the relation as 'ccof' Ora ‘and’ ccof ccof gayA ‘went’ ruke ‘stay’ k1 k2 k1 k7p mEM bAzAra loga ghara ‘I’ ‘market’ ‘people’ ‘home’ A subordinating conjunct will have a single child node

54. Some Problem Cases Certain complex sentences pose problems For example : agara tuma aate to hama vahaaM jaate ‘if’ ‘you’ ‘come’ ‘then’ ‘we’ ‘there’ ‘go’ “Had you come, we would have gone there” Counterfactual ‘agara’ and ‘to’ two connectives How to represent the dependencies ?

55. Main Clause – Subordinate Clause jaate ‘go+?’ ? ? K1 k7p agara to hama vahaaM ccof aate k1 tuma This representation fails to capture the relation between ‘agara’-’to’

56. Representation-Currently Followed to ‘then’ ccof jaate ‘go+?’ vmod k1 k7p agara hama vahaaM ccof ‘we’ ‘there’ aate 'come' k1 tuma 'you'

57. Alternative Proposal agara-to pof pof agara to ccof ccof aate jaate k1 k1 k7p tuma hama vahaaM Treat ‘agara-to’ as a complex conjunct

58. Ellipsis How to show dependencies when the head is missing ? bacce baDe ho gaye hEM kisI kI bAta nahIM sunate “The children have grown up, they don't listen to anyone”  No explicit conjunct !!  Insert a NULL element to show the dependencies NULL_CCP ccof ccof bade_ho_gaye nahIM_sunate Insert a NULL node only if it is essential to represent the dependencies.

59. Applying Paninian Model to English

60. Some English Examples English is :  A configurational language  Relatively fixed word order  Relations are not realised in affixes  Subject and object are positional  Subject is sacrosanct

61. Passive Rama ate a banana eat k1 k2 Rama banana A banana was eaten by Rama eat k2 k1 banana Rama  Extend the notion of vibhakti to English subject, object positions

62. Interrogatives Did Rama eat a banana ?  A 'Yes-no' interrogative Structurally,  Interrogative is realised through word order change  Subject – Auxiliary inversion  No interrogative morpheme

63. Interrogative Contd. Proposed solution: eat fragof k1 k2 Did Rama banana Position gives the cues for the constraints

64. Interrogatives Contd. What did Rama eat ? Eat k2 fragof k1 What did Rama  Question element 'what' and  Auxiliary position provide the syntactic cues

65. Control Verbs John persuaded Harry to leave p ersuade k1 k2 rt (?) ‏ John Harry leave  The object of persuade corefers to the 'missing' 'karta' of 'leave' John promised Harry to leave promise k1 k4 k2 John Harry leave  The subject of promise corefers to the 'missing' 'karta' of 'leave'

66. Verbs such as 'want' John wanted Harry to leave want k1 k2 John leave k1 Harry  'want' is a transitive verb and can take 'a clause' as its 'karma'

67. Empty 'it' It is raining in Delhi rain k7p Delhi  Possible representation  Empty 'it' can be captured in the feature structure

68. Conclusion Paninian Grammatical Formalism offers a depenency based approach for sentence parsing which suits better morphologically richer languages with relatively free word order such as Indian languages.