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Generic Feature Selection Methodology to Named Entity Detection from Indian and European LanguagesMALARKODI, C. S.   , DEVI, S. L. |
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Author keywords
classification, optimization, feature extraction, fuzzy logic, signal processing
References keywords
named(30), entity(28), recognition(23), language(13), languages(10), indian(9), india(8), sobha(6), natural(6), learning(6)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2019-02-28
Volume 19, Issue 1, Year 2019, On page(s): 79 - 88
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.01011
Web of Science Accession Number: 000459986900011
SCOPUS ID: 85064208532
Abstract
This paper describes the development of language and domain independent Named Entity Recognition (NER) system which can identify named entities from any given dataset irrespective of the language and domain. The main novelty of the present work is the generic feature selection methodology which has been applied to 7 Indian languages and 5 European languages. The generic feature selection methodology was done in two ways; first using frequency based approach; secondly k-means++ clustering algorithm was used to validate the patterns obtained in the frequency based approach. The dataset used for the experiments belongs to different genre. To the best of our knowledge we are the first to work on the development of cross-lingual Named Entity (NE) system with 12 languages belongs to different language families. We have done the 10-fold cross validation and the system output has been analyzed for all the languages and causes of error cases was discussed in the error analysis section. The performance of our system is also compared with the existing systems. |
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.
