Computational Paninian Grammar for Dependency Parsing

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Computational Paninian Grammar for Dependency
Parsing

• Dipti Misra Sharma
• LTRC, IIIT,
• Hyderabad
• NLP Winter School
• 25-12-2008

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Outline

• Backgrond
• Paninian Grammar The Basic Framework
• Some Example Cases
• Conclusion

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Background

• Indian languages
• Rich morphology
• Relatively flexible word order
• For example,
• 1. a) baccaa phala khaataa hai
• child fruit eathab
  pres
• b) phala baccaa khaataa hai
• c) phala khaataa hai baccaa
• d) baccaa khaataa hai phala

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Problems

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

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How to Draw PSs for 1 (c-d) ?

• 1 c) baccaa khaata hai phala
• 'child' 'eathab' 'pres' 'fruit'
• 1 d) phala khaata hai baccaa
• 'fruit' 'eathab' 'pres' 'child'
• Simple and perfectly natural sentences -
  difficult to handle in Phrase Structure
• Dependency structures make it easy

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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?filenamedownloa
  ds/nlpbook/index.html)

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Panian Grammar Contd.

• The grammar facilitates realisation of the
  intended meaning as an 'expression' of what the
  speaker wants to communicate (vivaksha)?

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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

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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)?

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Some Concepts

• Speaker's intention (vivakshaa)?
• Root Suffix (prakriti pratyaya)?
• Expectancy (aakaankshaa)?
• Eligibility (yogyataa)?
• Proximity (sannidhi)?
• Karaka
• vibhakti

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Speakers Intention (vivakshaa)?

• Each sentence reflects speakers 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

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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

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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

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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

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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'

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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

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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)?

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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

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Sub-actions - Opening of lock
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Sub-actions - Opening of lock

• 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

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Basic karaka relations

• Only six
• karta subject/agent/doer
• karma object/patient
• karana instrument
• sampradaan beneficiary
• apaadaan source
• adhikarana location in place/time/other

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Other relations

• Other dependency relations
• Purpose, reason, direction etc
• Causatives, associatives, comparatives etc
• Genitive, adjective

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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)

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However

• No one-to-one correspondence between relations
  and relation markers

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Syntactic Cues

• Verbal inflections (Tense Aspect Modality (TAM))?
• Passive verb agrees with the karma
• Some other cases
• raama ko jaanaa paDaa
• Ito go had to
• I had to go
• raama ko calanaa caahiye
• Ram to walk should
• I should leave

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Example

• Raama jaataa hai
• Ram gohab pres
• Ram goes
• jaa
• karta
• raama

Raama ko jaanaa paDaa Ramto go
had to Ram had to go jaa
karta mujha
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Some Examples

• Relative Clause
• MWEs
• Change of state verbs
• Conjuncts
• Ellipsis

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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 livehab
  pres tomorrow come
• rahaa hai
• prog pres
• My brother who lives in Delhi is coming
  tomorrow
• How to represent this ?
• Two possible representations

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Alternative 1

• aa
• meraa bhaaii kala
• jo
• raha
• dillii

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Alternative 2

• Aa
• meraa bhaaii kala
• coref
• raha
• jo dillii

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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

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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' 'dopassive
• Twenty people were registered for the workshop

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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' 'doperf' 'past
• The elements in a complex predicate can also be
  dis-continuous
• prashna to mohana ne kiyaa
  thaa
• 'question' 'part' 'Mohan' 'erg'
  'doperf' 'past'

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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 ?

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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

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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

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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.

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Idioms

• Two possible solutions
• phuuTii aazkha suhaa ltfs
  tamnahiiMtaa_thaagt
  not appealed
• k1 k2
• patnii vaha ciDiyaa
• wife that bird
• r6
• kisaana farmer
• Solution-1

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Idioms

• suhaa ltfs
  tamnahiiMtaa_thaagt not
• 
  appealed
• k2 pof k1
  
• vaha ciDiyaa phuuTii aazkha patnii
• that bird burst eye wife
  
• r6
• kisaana
• farmer'
• Solution-2

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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 ?

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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

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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 ?

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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

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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 ?

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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

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Representation-Currently Followed

• to then
• ccof
• jaate go?
• vmod k1
  k7p
• agara
  hama vahaaM
• ccof we
  there
• aate 'come'
• k1
• tuma 'you'

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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

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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

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Applying Paninian Model to English
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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

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Passive

• A banana was eaten by Rama
• eat ltfs tamwas_engt
• k2 k1
• banana Rama
• Extend the notion of vibhakti to English subject,
  object positions

• Rama ate a banana
• eat ltfs tamPASTgt
• k1 k2
• Rama banana

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Interrogatives

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

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Interrogative Contd.

• Proposed solution
• eat lt fs
  stypeinterrogative__yes-nogt
• fragof k1
  k2
• Did Rama
  banana
• Position gives the cues for the constraints

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Interrogatives Contd.

• What did Rama eat ?
• Eat lt fs stypeinterrogative_
  _whgt
• k2 fragof k1
• What did
  Rama
• Question element 'what' and
• Auxiliary position provide the syntactic cues

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Control Verbs

• John promised Harry to leave
• promise
• k1 k4 k2
• John Harry leave
• The subject of promise corefers to the 'missing'
  'karta' of 'leave'

• John persuaded Harry to leave
• persuade
• k1 k2 rt (?)?
• John Harry leave
• The object of persuade corefers to the 'missing'
  'karta' of 'leave'

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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'

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Empty 'it'

• It is raining in Delhi
• rain ltfs stypeexpletive__itgt
• k7p
  
• Delhi
• Possible representation
• Empty 'it' can be captured in the feature
  structure

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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.