Seminar presentations as part of the UGC Infrastructure Grants Project on Comeplex Predicates.

1. An Approach to the Automatic
Extraction of Complex Predicates in
Bengali
by
MEGHADITYA ROY CHAUDHURY
(BCSE- III)
Jadavpur University

2. What are Complex Predicates?
Complex Predicates are defined as predicates
which are composed of more than one
grammatical element (either morphemes/words),
each of which contributes a non-trivial part of the
non-
information of the complex predicate (Alex
Alsina 1996).
Complex Predicates contain (verb + verb) or
(noun/adjective + verb) combinations in South
Asian Languages (Hook, 1974).

3. Identifying Complex Predicates in
Bengali
Bengali is less computerized compared to
English due to its morphological enrichment.
As the identification of Complex Predicates
requires the knowledge of morphology, the task
of automatically extracting the Complex
Predicates is a challenge.

4. Benefits of Identification of
Complex Predicates
Detection and interpretation of complex
predicates are important for tasks such as
machine translation, information retrieval,
summarization etc.
A mere listing of complex predicates constitutes
valuable linguistic resource for lexicographers,
wordnet designers and other NLP system
designers.
designers.

5. Approach to the identification of
Complex Predicates
A Rule-Based Approach.
Rule-
In this project, I follow an algorithm for
automatic extraction of Complex
predicates from an untagged corpus using
only morphological analyzer and root
lexicon.

6. Approach to the Extraction of Complex
Predicates in Bengali Language
Complex Predicates in Bengali consists of
two types, Compound verbs and Conjunct
verbs.
Compound Verbs: Verb + Light Verb
Conjunct Verbs : Noun/Adj + Verb
The second verb is called Light Verb.

7. 16 Light Verbs in Bengali
aSa ‘come’ • dãRa ‘stand’
rakha ‘keep’ • ana ‘bring’
deoya ‘give’ • pOra ‘fall’
paTha ‘send’ • bERano ‘roam’
neoya ‘take’ • tola ‘lift’
bOSa ‘sit’ • oTha ‘rise’
jaoya ‘go’ • chaRa ‘leave’
phEla ‘drop’ • mOra ‘die’

8. Bengali Shallow Parser
The analysis begins at the morphological
level and accumulates at results of POS
tagger and chunker.
The final output combines the results of all
these levels and shows them in a single
representation (called Shakti Standard
Format).

9. The Console Output of the Bengali
Shallow Parser

10. Functions That Work in the
Background
Load_resource()
morph_file_creating()
Find_complex_predicate()
prepareOutput()
deleteFile()

11. Sample Run : Input File

12. Sample Run : Execution beginning

13. Sample Run : Execution Ends

14. Sample Run : Output

15. Conclusion
The algorithm heavily depends on The
Bengali Shallow Parser, hence it suffers
from some error crept in the parser tool.
This can be modified by reducing the
dependence and developing a more self- self-
sufficient algorithm .
It definitely calls for a large amount work in
future.