An Efficient Information Extraction Model for personal named entity
| ||International Journal of Computer Trends and Technology (IJCTT)|| |
|© - Issue 2013 by IJCTT Journal|
|Volume-4 Issue-3 |
|Year of Publication : 2013|
|Authors : Teena A.Sunny, G. Naveen Sundar|
Teena A.Sunny, G. Naveen Sundar "An Efficient Information Extraction Model for personal named entity"International Journal of Computer Trends and Technology (IJCTT),V4(3):446-449 Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: -Named entity recognition (NER) is one of the key techniques in language processing tasks such as information extraction. This paper focuses mainly on recognition of named entity using distance based clustering and attributes extraction patterns. The ultimate goal of the paper is to reduce ambiguity of person names with higher precision and recall and to avoid duplicity.
 Alexander Strehl, Joydeep Ghosh, and Raymond Mooney, “Impact of Similarity Measures on Web-page Clustering”, In the proceedings of AAAI Technical Report WS-00-01.
 Sheila Tejada, Craig A. Knoblock, Steven Minton, “Learning Domain Independent String Transformation Weights for High Accuracy Object Identification”, In proceedings of SIGKDD 2002.
 Artiles, Javier, Julio Gonzalo, Satoshi Sekine,“WePS 2 evaluation campaign: Overview of the web people search clustering task”, In 2nd web people search evaluation workshop, 18th www conference,(2009).
 Pedersen Ted, Kulkarn, nagha, “Unsupervised discrimination of person names in web contexts”, In Proceedings of the eighth international conference on intelligent text processing and computational linguistics, Mexico City, 2007.
 Sekine, Satoshi & Artiles, Javier, “WePS 2 evaluation campaign: overview of the web people search attribute extraction task”, In 18th www conference 2nd web people search evaluation workshop (WePS 2009).
Keywords — Unsupervised learning, precision, recall, Ambiguity, Bigrams, attribute extraction, clustering, tokens.