Genetic Algorithm (GA) in Feature Selection for CRF Based Manipuri Multiword Expression (MWE) Identification

TL;DR

This paper introduces a genetic algorithm approach to optimize feature selection for CRF-based Manipuri MWE identification, significantly improving precision and overall performance in NLP tasks.

Contribution

It presents a novel application of genetic algorithms for feature selection in Manipuri MWE identification, enhancing CRF model accuracy.

Findings

Achieved 86.84% precision in MWE identification.

Improved F-measure to 73.74% with GA-based feature selection.

Demonstrated the effectiveness of GA in optimizing NLP feature sets.

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.