Hindi Parsing

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Hindi Parsing

• Samar Husain
• LTRC, IIIT-Hyderabad,
• India.

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Outline

• Introduction
• Grammatical framework
• Two stage parsing
• Evaluation
• Two stage constraint based parsing
• Integrated data driven parsing
• Two stage data driven parsing

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Introduction

• Broad coverage parser for Hindi
• Very crucial
• MT systems, IE, co-reference resolution, etc.
• Attempt to make a hybrid parser
• Grammatical framework Dependency

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Introduction

• Levels of analysis before parsing
• Morphological analysis (Morph Info.)
• Analysis in local context (POS tagging, Chunking,
  case markers/postpositions computation)
• We parse after the above processing is done.

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Computational Paninian Grammar (CPG)

• Based on Paninis Grammar
• Inspired by inflectionally rich language
  (Sanskrit)
• A dependency based analysis (Bharati et al.
  1995a)
• Earlier parsing approaches for Hindi (Bharati et.
  al, 1993 1995b 2002)

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CPG (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

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karta karma karaka

• The boy opened the lock
• k1 karta
• k2 karma
• karta, karma usually correspond to agent, theme
  respectively
• But not always
• karakas are direct participants in the activity
  denoted by the verb
• For complete list of dependency relations (Begum
  et al., 2008)

open
k1
k2
boy
lock
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Hindi Parsing Approaches tried

• Two stage constraint based parsing
• Data driven parsing
• Integrated
• Two stage

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Two stage parsing

• Basic idea
• There are two layers (stages)
• The 1st stage handles intra-clausal relations,
  and the 2nd stage handles inter-clausal
  relations,
• The output of each stage is a linguistically
  sound partial parse that becomes the input to the
  next layer

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Stage 1

• Identify intra-clausal relations
• the argument structure of the verb,
• noun-noun genitive relation,
• infinitive-verb relation,
• infinitive-noun relation,
• adjective-noun,
• adverb-verb relations,
• nominal coordination, etc.

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Stage 2

• Identify inter-clausal relations
• subordinating conjuncts,
• coordinating conjuncts,
• relative clauses, etc.

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How do we do this?

• Introduce a dummy __ROOT__ node as the root of
  the dependency tree
• Helps in giving linguistically sound partial
  parses
• Keeps the tree connected
• Classify the dependency tags into two sets
• Tags that function within a clause,
• Tags that relate two clauses

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An example

• mai ghar gayaa kyomki mai bimaar
  thaa
• I home went because I sick
  was
• I went home because I was sick

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The parses
(a) 1st stage output, (b)
2nd stage final parse
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2 stage parsing

• 1st stage
• All the clauses analyzed
• Analyzed clauses become children of __ROOT__
• Conjuncts become children of __ROOT__
• 2nd stage
• Does not modify the 1st stage analysis
• Identifies relations between 1st stage parsed
  sub-trees

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Important linguistic cues that help Hindi parsing

• Nominal postpositions
• TAM classes
• Morphological features
• root of the lexical item, etc.
• POS/Chunk tags
• Agreement
• Minimal semantics
• Animate-inanimate
• Human-nonhuman

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Nominal postpositions and TAM

• rAma ø mohana ko KilOnA xewA hE
• Ram Mohana DAT toy
  give
• Ram gives a toy to Mohana
• rAma ne mohana ko KilOnA xiyA
• Ram ERG Mohana DAT toy gave
• Ram gave Mohan a toy
• rAma ko mohana ko KilOnA xenA
  padZA
• Ram DAT Mohana DAT toy had
  to give
• Ram had to give Mohan a toy
• The TAM dictates the postposition that appears on
  the noun rAma
• Related concept in CPG
• Verb frames and transformation rules (Bharati et
  al., 1995)

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Agreement

• rAma ø mohana ko KilOnA xewA hE
• Ram gives a toy to Mohana
• kaviwA ø mohana ko KilOnA xewI hE
• Kavita gives a toy to Mohana
• Verb agrees with rAma and kaviwA
• Agreement helps in identifying k1 and k2
• But there are some exceptions to this.

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Evaluation

• Two stage constraint based parser
• Data driven parsing
• Integrated
• 2 stage

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Constraint based hybrid parsing

• Constraint satisfaction problem (Bharati et al.
  2008a)
• Hard constraints
• Rule based
• Soft constraints
• ML
• Selective resolution of demands
• Repair
• Partial Parses

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Overall performance
UA L LA
CBP 86.1 65 63
CBP 90.1 76.9 75
MST 87.8 72.3 70.4
Malt 86.6 70.6 68.0
UA unlabeled attachments accuracy, L
labeled accuracy LA labeled attachment accuracy
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Error analysis

• Reasons for low LA
• Less verb frames
• Some phenomena not covered
• Prioritization errors

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Data driven parsing (Integrated)

• Tuning Malt and MST for Hyderabad dependency
  treebank (Bharati et al., 2008b)
• Experiments with different feature
• including minimal semantics and agreement

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Experimental Setup

• Data
• 1800 sentences, average length of 19.85 words,
  6585 unique tokens.
• training set 1178 sentences
• development and test set 352 and 363 sentences

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Experimental Setup

• Parsers
• Malt-version 1.0.1 (Nivre et al., 2007)
• arc eager
• SVM
• MST-version 0.4b (McDonald et al., 2005)
• Non-projective
• No. of highest scoring trees (k)5
• Extended feature set for both parsers

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Consolidated results
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Error analysis

• Reasons for low LA
• Difficulty in extracting relevant linguistic cues
• Agreement
• Similar contextual features Label bias
• Non-projectivity
• Lack of explicit cues
• Long distance dependencies
• Complex linguistic phenomena
• Less corpus size

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Observations

• Features that proved crucial
• TAM (classes) and nominal postpositions
• Minimal semantics
• Animate-inanimate
• Human-nonhuman
• Agreement
• After making it visible

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Data driven parsing 2 stage (Bharati et al.,
2009)

• MST parser
• Non-projective
• FEATS nominal and verbal inflections, morph
  info.
• Data
• 1492 sentences
• Training, development and testing 1200, 100 and
  192 respectively.

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Modular parsing

• Intra-clausal and Inter-clausal separately
• Introduce a dummy __ROOT__
• Parse clauses in 1st stage
• Then parse relations between clauses in 2nd stage

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Comparison with integrated parser
Details Accuracy Accuracy
Full (Stage1 Stage 2) LA 73.42
Full (Stage1 Stage 2) UA 92.22
Full (Stage1 Stage 2) L 75.33
Integrated LA 71.37
Integrated UA 90.60
Integrated L 73.35
There was 2.05, 1.62, 1.98 increase in LA, UA
and L respectively.
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Evaluation
Details Accuracy Accuracy
Stage1 (Intra-clausal) LA 77.09
Stage1 (Intra-clausal) UA 92.73
Stage1 (Intra-clausal) L 78.70
Stage2 (Inter-clausal) LA 97.84
Stage2 (Inter-clausal) UA 99.67
Stage2 (Inter-clausal) L 98.00
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Advantages

• Learning long distance dependencies becomes easy
• Stage 2 specifically learns them efficiently
• Few non-projective sentences
• Only intra-clausal ones remain
• Search space becomes local
• Handling complex sentences becomes easy

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Error analysis

• Reasons for low LA (in 1st stage)
• Unavailability of explicit cues
• Combining modular parsing with minimal semantics
  should help
• Difficulty in learning complex cues
• Agreement
• Similar contextual features Label bias
• Less corpus size

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References

• R. Begum, S. Husain, A. Dhwaj, D. Sharma, L. Bai,
  and R. Sangal. 2008. Dependency annotation scheme
  for Indian languages. In Proceedings of
  IJCNLP-2008.
• A. Bharati and R. Sangal. 1993. Parsing Free Word
  Order Languages in the Paninian Framework. Proc.
  of ACL93.
• A. Bharati, V. Chaitanya and R. Sangal. 1995a.
  Natural Language Processing A Paninian
  Perspective, Prentice-Hall of India, New Delhi.
• A. Bharati, A. Gupta and Rajeev Sangal. 1995b.
  Parsing with Nested Constraints. In Proceedings
  of 3rd NLP Pacific Rim Symposium. Seoul.
• A. Bharati, R. Sangal and T. P. Reddy. 2002. A
  Constraint Based Parser Using Integer Programming
  In Proc. of ICON-2002.
• A. Bharati, S. Husain, D. Sharma, and R. Sangal.
  2008a. A two-stage constraint based dependency
  parser for free word order languages. In
  Proceedings of the COLIPS IALP, Chiang Mai,
  Thailand.
• A. Bharati, S. Husain, B. Ambati, S. Jain, D.
  Sharma, and R. Sangal. 2008b. Two semantic
  features make all the difference in parsing
  accuracy. In Proceedings of the 6th ICON, Pune,
  India.
• A. Bharati, S. Husain, S. P. K. Gadde, B. Ambati,
  and R. Sangal. 2009. A modular cascaded partial
  parsing approach to complete parsing. In
  Submission.
• R. McDonald, F. Pereira, K. Ribarov, and J.
  Hajic. 2005. Non-projective dependency parsing
  using spanning tree algorithms. In Proc. of
  HLT/EMNLP, pp. 523530.
• J. Nivre, J. Hall, J. Nilsson, A. Chanev, G.
  Eryigit, S. Kübler, S. Marinov and E Marsi. 2007.
  MaltParser A language-independent system for
  data-driven dependency parsing. Natural Language
  Engineering, 13(2), 95-135.

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• Thanks!!