Constraint Based Hindi Parser LTRC, IIIT Hyderabad.

Constraint Based Hindi Parser

LTRC, IIIT Hyderabad

Introduction

Broad coverage parser Very crucial IL-IL MT systems, IE, co-reference resolution, etc.

Why Dependency ?

Phrase Structures Intrinsically presumes order Context Free Grammar (CFG) not well-suited for

free-word order languages (Shieber, 1985) Particularly ill suited to Indian Languages

Dependency Structures Gives flexibility Common structures With appropriate labels, closer to Semantics

Computational Paninian Grammar (CPG)

Based on Panini’s Grammar (500 BC) Inspired by Inflectionally rich language

(Sanskrit) A dependency based analysis

Computational Paninian Grammar (The Basic Framework)

Treats a sentence as a set of modifier-modified relations Sentence has a primary modified or the root

(which is generally a verb) Gives us the framework to identify these

relations Relations between noun constituent and verb

called ‘karaka’ karakas are syntactico-semantic in nature Syntactic cues help us in identifying the karakas

karta – karma karaka The boy opened the lock

k1 – karta k2 – karma

karta, karma usually correspond to agent, theme But not always

karakas are direct participants in the activity denoted by the verb

open

boy lock

k1 k2

Basic karaka relations karta – agent/doer/force

Relation label – k1 karma – object/patient

Relation label – k2 karana – instrument

Relation label – k3 sampradaan – beneficiary

Relation label – k4 apaadaan – source

Relation label – k5 adhikarana – location in place/time/other

Relation label – k7p/k7t/k7

For complete list of dependency relations: (Begum et al., 2008)

Basic karaka relations

raama phala khaataa hai ‘Ram eats fruit’

Basic karaka relations

raama chaaku se saiv kaatataa hai ‘Ram cuts the apple with knife’

Basic karaka relations

raama ne mohana ko pustaka dii‘Ram gave a book to Mohan’

Why Paninian Labels Other choices for labels could be

Grammatical relations Subject, Object, etc. Behavioral tests (Mohanan, 1994)

Thematic roles Agent, patient, etc. No concrete cues

Difficult to extract them automatically Karakas can be computationally exploited

Syntactically grounded, Semantically loaded Gives a level of interface

Levels of Language Analysis Morphological analysis (Morph Info.) Analysis in local context (POS tagging) Sentence analysis (Chunking, Parsing)

Semantic analysis (Word sense disambiguation, etc.)

Discourse processing (Anaphora resolution, Informational Structure, etc.)

Example

rAma ne mohana ko puswaka xI |

Example – Parsed Output

xI ‘give’

puswaka ‘book’

mohanarAma

k2k4k1

Parser

Two stage strategy Appropriate constraints formed

Stage I (Intra-clausal relations) Dependency relations marked Relations such as k1, k2, k3, etc. for each verb

Stage II (Inter-clausal relations & conjunct relations) Conjuncts, relative clauses, kriya mula, etc

Demand Frame for Verb

A demand frame or karaka frame for a verb indicates the demands the verb makes

It depends on the verb and its tense, aspect and modality (TAM) label.

A mapping is specified between karaka relations and vibhaktis (post-positions, suffix).

Karaka Frame

It specifies what karakas are mandatory or optional for the verb and what vibhaktis (post-positions) they take respectively

Each verb belongs to a specific verb class Each class has a basic karaka frame

Each TAM specifies a transformation rule

Example

rAma mohana ko puswaka xewA hE |

xewA hE ‘give is’

puswaka ‘book’

mohanarAma

k2k4k1

Parsed Dependency Tree

Transformations

Based on the TAM of the verb rAma ne mohana ko KilOnA xiyA | rAma ko mohana ko KilOnA xenA padZA | Appropriate transformation applied

Example

rAma ne mohana ko puswaka xI |

Karaka Frame – xe (give)

Transformation Rule – yA (TAM)

Karaka Frame

rAma ne mohana ko KilOnA xiyA |

yA TAM

----------------------------------------------------------------------------------------arc-label necessity vibhakti lextype src-pos arc-dir

---------------------------------------------------------------------------------------- k1 m ne n l c k2 m 0|ko n l c k3 d se n l c k4 d ko n l c----------------------------------------------------------------------------------------

Transformed frame for xe after applying the yA trasformation

0 ne

Parsed Output

xI ‘give’

puswaka ‘book’

mohanarAma

k2k4k1

Other frames

Adjectives

Steps in Parsing

Morph, POS tagging,Chunking

SENTENCE

Identify DemandGroups

Load Frames&

Transform

Find CandidatesApply

Constraints& Solve

Final Parse

Example:

rAma ne mohana ko KilOnA xiyA |

Identify the demand group,Load and Transform DF

xiyA Only verb

Transformed frame Use ‘yA’ TAM info.

----------------------------------------------------------------------------------------arc-label necessity vibhakti lextype src-pos arc-dir

---------------------------------------------------------------------------------------- k1 m ne n l c k2 m 0|ko n l c k3 d se n l c k4 d ko n l c----------------------------------------------------------------------------------------

Candidates

rAma ne mohana ko KilOnA xiyA _ROOT_ |

k1

k2

k4

k2

main

Constraints

C1: For each of the mandatory demands in a demand frame for each demand group, there should be exactly one outgoing edge labeled by the demand from the demand group.

C2: For each of the optional demands in a demand frame for each demand group, there should be at most one outgoing edge labeled by the demand from the demand group.

C3: There should be exactly one incoming arc into each source group.

Constraints

A parse of a sentence is obtained by satisfying all the above constraints

Ambiguous sentences have multiple parses Ill formed sentences have no parse.

Parse - I

rAma ne mohana ko KilOnA xiyA _ROOT_ |

k1

k4

k2

main

Parse - I

xiyA

KilOnAmohanarAma

k2k4k1

_ROOT_

main

Integer Programming Constraints

Xijk represents a possible arc from word group i to j with karaka label k

It takes a value 1 if the solution has that arc and 0 otherwise. It cannot take any other values.

The constraint rules are formulated into constraint equations.

Constraint Equations

C1: For each demand group i, for each of its mandatory demands k, the following equalities must hold:

Mik : j xikj = 1

C2: For each demand group i, for each of its optional or desirable demands k, the following inequalities must hold:

Oik:j xikj <= 1

C3: For each of the source groups j, the following equalities must hold:

Sj : ik xikj = 1

Multiple Frames

If more than one karaka frame for a verb Call Integer Programming package for each

frame If more than one demand groups (e.g.,

multiple verbs) in the sentence with multiple demand frames Call Integer Programming package for each

combination of such frames

Other frames

Common karaka frame Attached to each karaka frame Preference given to main frame if there are

clashes

Fallback karaka frame required karaka frame is missing Graceful degradation

Stage I: Types being handled

Simple Verbs Non-finite verbs

wA_huA wA_hI nA kara 0_rahe, etc.

Copula Genitive

Example (Complex Sentence)

rAma ne phala khaakara mohana ko

Ram ‘ERG’ fruit ‘having eaten’ Mohan ‘DAT’

KilOnA xiyA

toy gave

‘Having eaten the fruit Ram gave the toy to Mohan’

Candidates

rAma ne phala khaakara mohana ko KilOnA xiyA _ROOT_ |

X1: k1

X3: k2

X5: k4

X2: k2

X7: vmodX4: k2

X6: k2

X8: main

Constraint Equations Verb ‘xe’

Mandatory Demands (C1) k1 x1 = 1 k2 x2 + x3 + x4 = 1

Optional Demands (C2) k4 x5 <= 1

Verb ‘khaa’ Mandatory Demands (C1)

k2 x6 = 1 vmod x7 = 1

_ROOT_ C1

Main x8 = 1

Constraint Equations (contd.) Incoming Arcs into Source (C3)

rAma x1 = 1

phala x4 + x6 = 1

khaa x7 = 1

mohana x3 + x5 = 1

KilOnA x2 = 1

xe x8 = 1

Solution Graph

xiyA

KilOnAmohanarAma

k2k4k1

_ROOT_

main

khaakara

phala

k2

vmod

References Akshar Bharati and Rajeev Sangal. 1993. Parsing free word order languages in

Paninian Framework. ACL:93, Proc.of Annual Meeting of Association of Computational Linguistics, Association of Computational Linguistics, New Jersey. USA.

Akshar Bharati, Rajeev Sangal, T Papi Reddy. 2002. A Constraint Based Parser Using Integer Programming In Proc. of ICON-2002: International Conference on Natural Language Processing.

Rafiya Begum, Samar Husain, Arun Dhwaj, Dipti Misra Sharma, Lakshmi Bai and Rajeev Sangal. 2008. Dependency Annotation Scheme for Indian Languages. In Proceedings of The Third International Joint Conference on Natural Language Processing (IJCNLP). Hyderabad, India.

S. M. Shieber. 1985. Evidence against the context-freeness of natural language. In Linguistics and Philosophy, p. 8, 334–343.

Tara Mohanan, 1994. Arguments in Hindi. CSLI Publications.

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