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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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Generic Feature Selection Methodology to Named Entity Detection from Indian and European Languages

MALARKODI, C. S. See more information about MALARKODI, C. S. on SCOPUS See more information about MALARKODI, C. S. on IEEExplore See more information about MALARKODI, C. S. on Web of Science, DEVI, S. L. See more information about DEVI, S. L. on SCOPUS See more information about DEVI, S. L. on SCOPUS See more information about DEVI, S. L. on Web of Science
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,279 KB) | Citation | Downloads: 498 | Views: 1,195

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
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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.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 196 TCR
SCOPUS® Citations for all references: 1,504 TCR

Web of Science® Average Citations per reference: 5 ACR
SCOPUS® Average Citations per reference: 40 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-04-16 21:56 in 108 seconds.




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