The document describes a system for resolving pronominal anaphora in Nepali language texts. It implements the Lappin and Leass algorithm using a hand-annotated Nepali corpus. The system achieved an accuracy of 64% on test data containing sentences of varying lengths. While limited by the lack of part-of-speech tagging and parsing, the research demonstrated that basic anaphora resolution is possible in Nepali and could inform future work on more sophisticated systems utilizing statistical methods.

1. Pronominal Anaphora
Resolution
in Nepali Language
by
Dev Bahadur Poudel(03314)
Bivod Aale Magar(03307)
Nepal Engineering College
Changunaryan, Bhaktapur
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2. Contents
 BriefIntroduction and Background
 Approach to Algorithm
 Implementation in Nepali Discourse
 Over-view of our system
 Scope of our system
 Conclusion
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3. What is Anaphora?
Reference to an entity that has
been previously introduced in the
discourse.
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4. What is Anaphora Resolution?
Process of determining the
antecedent of an anaphor.
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5. Anaphor resolution in Nepali
राम सकू ल जानछ । ऊ घर फकर नछ ।
Anaphor
Antecedent
ऊ =राम
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6. Can Machine resolve the anaphora?
 Human intelligence can easily find out to
which referents the anaphor belongs.
 Can we built a system that can resolve the
anaphora to the antecendents?
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7. Corpus
 collection of linguistic data, either written
texts or a transcription of recorded speech,
which can be used as a starting-point of
linguistic description or as a means of
verifying hypotheses about a language.
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8. Unicode
 an industry standard allowing computers to
represent and manipulate text consistently
 consists of about 100,000 characters, a set of code
charts for visual reference, an encoding methodology
and set of standard character encodings
 Unlike ASCII, which uses 7 bits for each character,
Unicode uses 16 bits, which means that it can
represent more than 65,000 unique characters.
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9. Approach to the Algorithm
 Non-Probabilistic
– Lappin and Leass Algorithm(1994)
– A Tree Search Algorithm- Hobbs(1978)
 Probabilistic
– Centering Algorithm
– Mitkov’s weak knowledge algorithm
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10. Approach to the Algorithm
 Lappin and Leass Algorithm(1994)
 Algorithm based on the Sailence
factors given to the noun and pronoun.
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11. Salience factors in Lappin and
Leass's Algorithm.
 Sentence recency 100
 Subject emphasis 80
 Existential emphasis 70
 Accusative (direct object) emphasis 50
 Indirect object and oblique complement
emphasis 40
 Non-adverbial emphasis 50
 Head noun emphasis 80
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12. Implementation
Can be implemented using different
languages
JAVA, PHP
Our system uses JAVA
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13. Block Diagram of the system
Tokenizer and Salience Output
Input Tagger Factor
Assigner
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14. Flowchart START
Input Paragraph
Take A sentence
Tokenize
Take token
no
Log Error Check In Corpus
yes
Classify as noun or pronoun
Classify subject/Object
Give Silence value
Calculate total weights
yes
Next sentence ? Half the salience values
no
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Display Results
Pronominal Anaphora Resolution
END

15. User Interface
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16. An Example in Nepali
! = /fd 38L lsGg rfxG5 .
@= xl/n] Tof] k;ndf b]Vof] .
#= p;n] p;nfO{ b]vfof] .
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17. ! = /fd 38L lsGg rfxG5 .
Decrease the salient values by factor 2
when reading next sentence
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18. @= xl/n] Tof] k;ndf b]Vof] .
 xl/ gets (Rec: 100+ Sub: 80+ Non adv:
50+ HN:80 =310)
Tof] get 280 (rec:100+ cobj:50+non-
adv:50+ HN: 80)
Tof] resolved to 38L due to high salience
value of 38L
 k;n will get (rec:100+non-adv 50+
HN:80)=230
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19. Updated Discourse Model
Divide the previous salience factors by two
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20. #= p;n] p;nfO{ b]vfof] .
 p;n] will be resolve to xl/ due to high salience factors. Add Salience
factor (recency:100+ subpos: 80+ nonadv:50+HN:80)=310
 p;nfO{ can not be xl/ due to syntactic constraints. So, p;nfO{ will
be resolved to /fd . (rec:100+indObj:40+non-adv 50+ HN:80)=270
Updated Discourse Model
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21. Result
Paragraph Total Total Total Correctly Incorrectly Zero Effici
Samples Antecedent Anaphors resolved Resolved Anapho ency
Using
Used s rs
2-sentence 15 37 22 15 7 0 68%
3-sentence 15 50 37 28 9 0 75%
4-sentence 10 35 35 22 11 2 62.8%
5-sentence 10 43 41 25 14 2 60.9%
> 5-sentence 5 28 31 17 11 3 54%
Total 55 193 166 107 52 7 64%
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22. Scope of the Project
-Natural language
processing
-Question answering
-Text Summarizing
-Information Extraction
-Interaction with query
interfaces and dialogue
interpretation
22 -Natural Language
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23. Limitations
 The lack of tagger and parser limits the system for
large corpus and had to go for a hand annotated
corpus.
 The sentences are limited to the words defined in
our corpus
 The system is limited to the third person pronouns
but not reflexive.
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24. Further Works
 Morphological analysis can be done
 The system can be enhanced further work on large
number of sentences.
 This project can be used with collaboration of other
NLP projects in Nepali language for further research.
 The statistical methods can be applied to get higher
efficiency.
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25. Conclusion
 Research to see how a basic approach like
Lappin and Leass performs for Nepali language.
 Applies to non reflexive third person pronouns.
 Emerging concept in Nepali Language
 Understanding the discourse - challenging to
computer intelligence
 Without tagger and parser our system is greatly
dictionary dependent
 Our work aid to future research in Nepali
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26. Thank You.
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