Paper Info
Title
Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri Multiword Expression (MWE) Identification
Abstract
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.
Year
DOI
Venue
2011
10.5121/ijcsit.2011.3505
International Journal of Computer Science and Information Technology
Keywords
Field
DocType
natural language processing,information retrieval,cross validation,fitness function,evolutionary computing,machine translation,feature selection,conditional random field,question answering,genetic algorithm
Data mining,Feature selection,Computer science,Machine translation,Natural language processing,Artificial intelligence,Multiword expression,Genetic algorithm,Conditional random field,Question answering,Agglutinative language,Speech recognition,Fitness function,Machine learning
Journal
Volume
ISSN
Citations 
abs/1111.2399
International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 5, Oct 2011, pp 53-66
1
PageRank 
References 
Authors
0.39
3
2
Name
Order
Citations
PageRank
Kishorjit Nongmeikapam1196.68
Sivaji Bandyopadhyay2929107.30