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Named Entity Recognition using Statistical Model Approach

by Pyari Padmanabhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 14
Year of Publication: 2013
Authors: Pyari Padmanabhan
10.5120/12810-0066

Pyari Padmanabhan . Named Entity Recognition using Statistical Model Approach. International Journal of Computer Applications. 73, 14 ( July 2013), 31-33. DOI=10.5120/12810-0066

@article{ 10.5120/12810-0066,
author = { Pyari Padmanabhan },
title = { Named Entity Recognition using Statistical Model Approach },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 14 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number14/12810-0066/ },
doi = { 10.5120/12810-0066 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:40:05.549354+05:30
%A Pyari Padmanabhan
%T Named Entity Recognition using Statistical Model Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 14
%P 31-33
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Named Entities (NE) are atomic elements like names of person, places, locations, organizations, quantity etc. Named Entity Recognition is a classification problem. It involves the task of identifying and classifying certain elements in text into predefined categories of named entities. Main subtasks for the Named Entity Recognition involves (1) The Document corpus construction (2) The preprocessing of the documents (3) Determine the contexts (4) Applying the hidden Markov model. In this paper, the hidden Markov model is adopted for the purpose of effective recognition of Named Entities from a document corpus.

References
  1. Wahiba Ben Abdessalem Karaa, Named Entity Recognition Using Web Document Corpus, International Journal Of Managing Information Technology (IJMIT) Vol. 3, No. 1, February 2011,pp 46-56.
  2. F. Denis, R. Gilleron, and F. Letouzey, Learning from positive and unlabeled examples. Elsevier. Theoretical Computer Science, 2005, vol. 348, pp. 70 – 83.
  3. O. Etzioni, M. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. Weld, and A. Yates, Unsupervised named-entity extraction from the web: An experimental study, Artificial Intelligence, 2005, vol. 65,pp. 91–134.
  4. M. Collins and Y. Singer, Unsupervised models for named entity classification, in Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999, pp. 189–196.
  5. C. Krstev, D. Vitas, D. Maurel, M. Tran, Multilingual ontology of proper name, in Proceedings of the Language and Technology Conference, pp. 116–119, Poznan, Poland, 2005.
  6. A. Mikheev, M. Moens, and C. Grover, Named Entity Recognition without Gazetteers, in Proceedings of Conference of European, Chapter of the Association for Computational Linguistics, EACL '99, pp. 1-8, University of Bergen, Bergen, Norway June 1999.
  7. G. S. Mann, Fine-grained proper noun anthologies for question answering, International Conference on Computational Linguistics, COLING-02 on SEMANET: building and using semantic networks, 2002, Vol. 11
  8. D. Nadeau, P. D. Turney, and S. Matwin, Unsupervised named-entity recognition: Generating gazetteers and resolving ambiguity, Lecture Notes in Computer Science, Springer, 2006, pp. 266–277, Berlin Heidelberg 2006. rs Ltd.
  9. B. Favre, F. Béchet, and P. Nocéra, Robust Named Entity Extraction from Spoken Archives, in Proceedings of HLT-EMNLP'05, pp. 491-498, Vancouver, Canada, October 2005.
  10. Wee Sun Lee, Bing LiuLearning with Positive and Unlabeled Examples Using Weighted Logistic Regression Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 2003.
  11. Jiafeng Guo†, Gu Xu‡, Xueqi Cheng†, Hang Li‡, Named Entity Recognition in Query , 2009
Index Terms

Computer Science
Information Sciences

Keywords

Hidden Markov model preprocessing

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