97 research outputs found
Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri Multiword Expression (MWE) Identification
This paper deals with the identification of Multiword Expressions (MWEs) in
Manipuri, a highly agglutinative Indian Language. Manipuri is listed in the
Eight Schedule of Indian Constitution. MWE plays an important role in the
applications of Natural Language Processing(NLP) like Machine Translation, Part
of Speech tagging, Information Retrieval, Question Answering etc. Feature
selection is an important factor in the recognition of Manipuri MWEs using
Conditional Random Field (CRF). The disadvantage of manual selection and
choosing of the appropriate features for running CRF motivates us to think of
Genetic Algorithm (GA). Using GA we are able to find the optimal features to
run the CRF. We have tried with fifty generations in feature selection along
with three fold cross validation as fitness function. This model demonstrated
the Recall (R) of 64.08%, Precision (P) of 86.84% and F-measure (F) of 73.74%,
showing an improvement over the CRF based Manipuri MWE identification without
GA application.Comment: 14 pages, 6 figures, see
http://airccse.org/journal/jcsit/1011csit05.pd
A Transliteration of CRF based Manipuri POS Tagging
AbstractTransliteration is common to all those language which have multiple scripts. Manipuri, which is one of the Schedule Indian Languages, is one of them. This language has two scripts: a borrowed Bengali Script and the original Meitei Mayek (Script). Part of Speech (POS) tagging of the Bengali Script Manipuri text is performed using Conditional Random Field (CRF) which is then followed by the transliteration to Meitei Mayek
Multi-Engine Approach for Named Entity Recognition in Bengali
PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200
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