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Intuitive Approaches for named Entity Recognition and Classification: A Survey

Published on January 2014 by Mukta Takalikar, Manali Kshirsagar, Gauri Dhopavkar
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International IT Summit Confluence 2013-The Next Generation Information Technology Summit
Foundation of Computer Science USA
CONFLUENCE2013 - Number 2
January 2014
Authors: Mukta Takalikar, Manali Kshirsagar, Gauri Dhopavkar
5db9ef73-2655-4ea2-9ca0-0d2ff0593b89

Mukta Takalikar, Manali Kshirsagar, Gauri Dhopavkar . Intuitive Approaches for named Entity Recognition and Classification: A Survey. International IT Summit Confluence 2013-The Next Generation Information Technology Summit. CONFLUENCE2013, 2 (January 2014), 35-38.

@article{
author = { Mukta Takalikar, Manali Kshirsagar, Gauri Dhopavkar },
title = { Intuitive Approaches for named Entity Recognition and Classification: A Survey },
journal = { International IT Summit Confluence 2013-The Next Generation Information Technology Summit },
issue_date = { January 2014 },
volume = { CONFLUENCE2013 },
number = { 2 },
month = { January },
year = { 2014 },
issn = 0975-8887,
pages = { 35-38 },
numpages = 4,
url = { /proceedings/confluence2013/number2/15123-1316/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International IT Summit Confluence 2013-The Next Generation Information Technology Summit
%A Mukta Takalikar
%A Manali Kshirsagar
%A Gauri Dhopavkar
%T Intuitive Approaches for named Entity Recognition and Classification: A Survey
%J International IT Summit Confluence 2013-The Next Generation Information Technology Summit
%@ 0975-8887
%V CONFLUENCE2013
%N 2
%P 35-38
%D 2014
%I International Journal of Computer Applications
Abstract

The survey of research in the field of Named Entity Recognition and Classification (NERC) features, techniques and evaluation methods, is presented, though it is not extensive and may not cover all the languages. It gives the depth of previous work in the field. Automatic named entity recognition and classification in the text surely improves the quality of results while searching the web. Highly accurate Named Entity Recognition (NER) is a challenge even today. The output of NER system is used for question answering, document clustering, document summarization [9].

References
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  2. Bikel, D. M. , Schwartz, R. , Weischedel, R. M. 1999. An algorithm that learns what's in a name. Machine Learning, 34, 211–231. doi:10. 1023/A: 1007558221122
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  5. Ekbal, A. , & Bandyopadhyay, S. 2010. Improving the performance of a NER system by post-processing and voting. In Structural, Syntactic and Statistical Pattern Recognition, LNCS 5342 (pp. 831?841), Springer.
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  8. Nadeau, D. , & Sekine, S. 2007. A survey of named entity recognition and classification. Lingvisticae Investigationes, 30, 3–26. doi:10. 1075/li. 30. 1. 03nad
  9. Palshikar, G. K. , 2011. Techniques for named entity recognition: a survey. TRDDC Technical Report, pp. 191-217.
  10. Thelen, M. and Riloff E. 2002. A bootstrapping method for learning semantic lexicons using extraction pattern contexts. Conference on Empirical Methods in natural Language Processing (EMNLP 2002).
  11. Talukdar, P. , Brants, T. , Liberman, M. , & Pereira, F. 2006. A context pattern induction method for named entity extraction . In proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-2006), (pp. 141–148).
Index Terms

Computer Science
Information Sciences

Keywords

Named Entity Recognition Named Entity Extraction Natural Language Processing