Dependency Grammar

Samar Husain

Introduction

• HistoryHistory– Indian grammatical tradition: Panini

Medieval theories: Arabic grammatical tradition– Medieval theories: Arabic grammatical tradition– Classical and Slavic grammatical tradition

M d T i M l k– Modern: Tesniere, Melcuk

• No single dependency formulation– Diverse

Basic definition

• Syntactic structure consists of lexical elementsSyntactic structure consists of lexical elements linked by binary asymmetrical relations called dependenciesdependencies.

Sample Dependency Tree (Unlabelled)(Unlabelled)

Sample Dependency Tree (labelled)

Dependency Grammar Formalisms

• TesniereTesniere• Word Grammar

i l G i i i ( G )• Functional Generative Description (FGD)• Computational Paninian Grammar (CPG)• Extensible Dependency Grammar (XDG)

Core Characteristics

• Binary asymmetrical relationsy y

• Identifying dependency heads; H→D (in construction C)– H determines the syntactic category of C and often

replace Creplace C– H determines the semantic category of C; D gives

semantic specification.H i bli t d D ti l– H is obligatory and D optional

– H selects D and determines whether D is obligatory or optionalp

• The form of D depends on H (Agreement etc.)p ( g )• Linear position of D is specified with reference

to H

• Prototypical case with max properties• Others have distinguished between dependent

typeM h l i l– Morphological

– Semantic– Syntactic– Syntactic

• Head – Compliment vs Head – ModifierHead Compliment vs. Head Modifier– Endo-centric construction

• head can replace the whole without disrupting the• head can replace the whole without disrupting the syntactic structure

– Exo-centric construction• the head cannot readily replace the whole

– Valency: Requirement on its syntactic dependent y q y pthat reflects its interpretation as a semantic predicate

Flavors

• LayersLayers– Mono vs. Multi

Deep vs Surface– Deep vs. Surface• Representation

– Tree, Graph, etc.• Nodes• Dependency structure and word order

– Projectivityj y

Layers in MTT and FGD

Layers in PDT

Some non-projective structures in HindiHindi

Computational Paninian Grammar

• CPG is based on the Panini’s grammarCPG is based on the Panini s grammar– It is a dependency grammar

• CPG is primarily concerned with:– how the information is coded and– how it can be extracted

Core concepts

• aakaankshaaaakaankshaa– The verb’s core requirements in order for it to be

meaningful is the aakaankshaa of the verb. It can be roughly translated as the ‘argument structure’ of the verb.

• karakak h i i i h i ifi d b h– Karakas are the participants in the action specified by the

verb. These relations as mentioned earlier are syntactic-semantic in nature, in that they are syntactically grounded , y y y gbut also convey some meaning.

• AbhihitaAbhihita– The notion of abhihita signifies the karaka expressed by a

verbal TAM. This can be roughly translated as ‘agreement’. So for example, the main verb can point to the karta karaka by agreeing with it.

• Vibhakti (Sup Ting)• Vibhakti (Sup, Ting)– Vibhakti is an abstract concept used for signifying the case

markings on the nouns and the tense, aspect and modality g , p yof verbs. The former is called sup and the later is called ting.

• YogyataaYogyataa– Yogyataa can be roughly translated as semantic

selectional restriction of the verbselectional restriction of the verb.

Vi k h• Vivaksha– Signifies the intention of the speaker that lead to

diff i i f ti di d i th tdifference in information encoding during the act of speaking.

Salient Properties of CPG

• Layersy– Morphological– Local dependencies– karaka (participants in an actions)

• Representation of dependency– Labeled tree– Node

• Le ical/phrasal• Lexical/phrasal

– Labels• Syntactico-semantic (+others)

• Non-projectivity– Allowed

Layers in CPG

Morphological Layer (SSF representation)representation)

rAma ne mohana ko nIlI kiwAba xI‘Ram’ ERG ‘Mohan’ ACC ‘blue’ ‘book’ ‘gave’‘Ram gave Mohan a blue book’

Local dependencies (POS and chunks)

Dependency Tree (SSF representation)

Expanded Dependency Tree

rAma ne mohana ko nIlI kiwAba xI‘Ram’ ERG ‘Mohan’ ACC ‘blue’ ‘book’ ‘gave’Ram ERG Mohan ACC blue book gave‘Ram gave Mohan a blue book’

Selected dependency labels [TOTAL = 43 ]

k1 karta (similar to agent/doer)k2 karma (similar to patient/theme)k3 instrumentk4 beneficiaryk4 beneficiaryk5 sourcek7t temporal locationk7p spatial locationk1s noun complement k2p destinationpk1, mk1, jk1 Causer, mediator-causer, causee rh causert purposert purposersp durationadv adverb (manner)

f f ( l di )pof Part-of (complex predicates)ccof conjunctionfragof fragment-of

k1 karta (similar to agent/doer)k2 karma (similar to patient/theme)

The first is the class of all k-

l b l i l d

k3 instrumentk4 beneficiaryk5 source

labels; includes k1-k7*

k7t temporal locationk7p spatial locationk1s noun complementk1s noun complement k2p destinationpk1, mk1, jk1

Causer, mediator-causer, causee jk1rh causert purposersp durationnmod Noun modificationpof Part-of (complex predicates)p ( p p )ccof conjunctionfragof fragment-of

k1 karta (similar to agent/doer)k2 karma (similar to patient/theme)k3 instrumentk4 beneficiaryk5 sourcek7t temporal locationk7p spatial locationk1s noun complementk1s noun complement k2p destinationpk1, mk1, jk1

Causer, mediator-causer, causee

Second is the class of r-labels,

jk1rh causert purpose

including rh, rt, rsp, rd etc.

rsp durationnmod Nmod (noun modificationpof Part-of (complex predicates)p ( p p )ccof conjunctionfragof fragment-of

k1 karta (similar to agent/doer)k2 karma (similar to patient/theme)

•Third, the class of modifier labels: i l d d

k3 instrumentk4 beneficiaryk5 source

includes nmod, vmod, jjmod

k7t temporal locationk7p spatial locationk1s noun complementk1s noun complement k2p destinationpk1, mk1, jk1

Causer, mediator-causer, causee jk1rh causert purposersp durationnmod Noun modificationpof Part-of (complex predicates)p ( p p )ccof conjunctionfragof fragment-of

k1 karta (similar to agent/doer)k2 karma (similar to patient/theme)

•Finally, the class of non-

k3 instrumentk4 beneficiaryk5 source of non

dependencies consisting of pof,

k7t temporal locationk7p spatial locationk1s noun complement g p

fragof, ccof k1s noun complement k2p destinationpk1, mk1, jk1

Causer, mediator-causer, causee jk1rh causert purposersp durationnmod noun modificationpof Part-of (complex predicates)p ( p p )ccof conjunctionfragof fragment-of

•We get a 4-way k1 karta (similar to agent/doer)k2 karma (similar to patient/theme)

classification:-k-labelsr labels

k3 instrumentk4 beneficiaryk5 source -r-labels

-modifier labels-other (non

k7t temporal locationk7p spatial locationk1s noun complement other (non

dependencies)k1s noun complement k2p destinationpk1, mk1, jk1

Causer, mediator-causer, causee jk1rh causert purposersp durationnmod noun modificationpof Part-of (complex predicates)p ( p p )ccof conjunctionfragof fragment-of

Intransitive verb

rAma bETA hE‘Ram’ ‘sit’ PRES‘Ram is sitting’

Transitive verb

rAma Kira KAwA hE‘Ram’ ‘sweet’ ‘eat’ PRES‘Ram eats sweets’

Transitive verb

rAma rojZa eka seba KAwA hEj‘Ram’ ‘daily’ ‘an’ ‘apple’ ‘eat’ PRES‘Ram eats an apple everyday’

Complex Predicate

ve rAjanIwi para carCA kara rahe We‘They’ ‘politics’ ON ‘discussion’ ‘do’ CONT PRES‘They were discussing politics’

Clausal Complementvaha mAnawA hE ki rAma buxXimAna hE‘He’ ‘believe’ PRES ‘that’ ‘Ram’ ‘intelligent’ ‘is’‘He believes that Ram is intelligent’He believes that Ram is intelligent

ParticiplerAma ne KAnA Kakara pAnI piyA‘Ram’ ERG ‘food’ ‘having eaten’ ‘water’ ‘drank’‘Having eaten the food Ram drank water’Having eaten the food Ram drank water

Coordination

rAma seba KAwA hE Ora siwA dUXa pIwI hEp‘Ram’ ‘apple’ ‘eat’ PRES ‘and’ ‘sita’ ‘milk’ ‘drink’ PRES ‘Ram eats apples and Sita drinks milk’

Coordination

rAma Ora SyAma skUla jAwe hE‘Ram’ ‘and’ ‘Shyam’ ‘school’ ‘go’ PRES‘Ram and SyAma go to school’

Relative Clausejo ladZakA vahAz bETA hE vaha mere skUla meM paDZawa hEjo ladZakA vahAz bETA hE vaha mere skUla meM paDZawa hEREL boy ‘there’ ‘sit’ PRES ‘he’ ‘my’ ‘school’ IN ‘study’ PRES‘The boy who is sitting there studies in my school’

Gapping

rAma xilli gayA Ora SyAma AgarA‘Ram’ ‘Delhi’ ‘went’ ‘and’ ‘Shyam’ ‘Agra’Ram Delhi went and Shyam Agra‘Ram went to Delhi and Shyam Agra.’

Phrase Structure Grammar

Phrase Structure Grammar and Dependency GrammarDependency Grammar

• PSGPSG– Constituency

• Similar syntactic enviornment– Eg. NP followed by a verb

• Can be moved around together, but cannot be broken• Substitution• Coordination

– Functional categories– Knowledge of language relies on the structural relationship

in the sentence rather than on the sequence of the words– Notion of headNotion of head

• Indirect access to relationsIndirect access to relations• Indirect access to heads

• DGDG– Direct access of relations between elements– Transparent argument structure of the verbTransparent argument structure of the verb– Intuitive analysis

• Extremely popular in the CL/NLP community since few yearsy– Especially for free word order morphologically

rich languages

Issues

• Do DG and PSG complement each other?Do DG and PSG complement each other?• Are there some generalizations that are not

captured by individual grammaticalcaptured by individual grammatical frameworks?C bi h l i f DS d PSG• Can we combine the analysis of DS and PSG to get to a better understanding of structures

d h i l ?and phenomenons in a language?• Are some phenomenon better understood in

specific grammar ?

Generative capacity: PSG and DG

References

- A. Bharati, D. M. Sharma, S. Husain, L. Bai, R. Begam and R. Sangal. 2009 A C T B k f I di L G id li f2009. AnnCorra: TreeBanks for Indian Languages, Guidelines for Annotating Hindi TreeBank.

- A. Bharati, V. Chaitanya and R. Sangal. 1995. Natural Language Processing: A Paninian Perspective. Prentice-Hall of India, New Delhi.

- S. Kubler, R. McDonald and J. Nivre. 2009. Dependency parsing. Morgan and Claypool.

- Marco Kuhlmann. 2007. Dependency Structures and Lexicalized Grammars PhD thesis Saarland University Saarbrücken GermanyGrammars. PhD thesis, Saarland University, Saarbrücken, Germany.

- J. Nivre, 2005. Dependency Grammar and Dependency Parsing. MSI report05133. Växjö University: School of Mathematics and Systems Engineering.

- Z. Žabokrtský. 2005. Resemblances between Meaning-Text Theory and F i l G i D i i I P d f 2 d I lFunctional Generative Description. In Proceedings of 2nd International Conference of Meaning-Text Theory, Moscow, 2005.

- http://ufal.mff.cuni.cz/pdt2.0/doc/pdt-guide/en/html/ch02.html