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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(4): 443-445 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.163

Signet: Web Information Retrieval with NE Disambiguation based on HMM and CRF

S. Balaji and S. Sasikala

Abstract—As compared to many other techniques used in natural language processing, hidden markov models (HMMs) are an extremely flexible tool and has been successfully applied to a wide variety of information extraction tasks. This work focus on webpage perceptive through model of Hierarchical Conditional Random Fields (i.e. HCRF) and offer results in free text segmentation and labelling. This paper specially addresses the problem of research community of academic people integration (SIGNET-similar interest group) through perceiving the entities of them.

Index Terms—HMM, HCRF, Named-Entity, SIGNET.

S. Balaji is with Sengunthar Engineering College, Tiruchengode, Tamilnadu, India (e-mail: hereiambalaji@yahoomail.com).
S. Sasikala was with KSR College of arts and science, Tiruchengode, Tamilnadu, India. She is now with the Department of Computer Science (e-mail: sasi_sss123@rediff.com).


Cite: S. Balaji and S. Sasikala, "Signet: Web Information Retrieval with NE Disambiguation based on HMM and CRF," International Journal of Machine Learning and Computing vol. 2, no. 4, pp. 443-445, 2012.

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