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

2D-ACAR Studies on Swift Heavy Ion Si-Implanted GaAs

AbstractMaterial properties modification by high energy heavy ion implantation is a prospective technology leading to many device fabrications. This technique induces defects and hence the physical properties of the materials are modified. The effects of swift heavy ion implantation induced defects by 120 MeV 28+Si ion implantation and doping in SI-GaAs are presented from the electron momentum distribution (EMD) of vacancy-type defects studied by two-dimensional angular correlation of annihilation radiation (2D-ACAR). The positron trapping due to the influence of high-energy Si- implantation in GaAs (n-type) is compared with the corresponding spectra of SI- GaAs and with Si-doped (n-type) GaAs. The EMD of the implanted sample shows a distinct increased isotropic distribution with a characteristic transform of its structure as evident from the low momentum region compared to the pristine sample. The characteristics of defects created by Si doping and by 120 MeV 28+Si ion implantation of undoped semi-insulating (SI) GaAS are discussed. These results indicate the nature of positron trapping in open volume defects such as vacancy clusters created by implantation