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: firstname.lastname@example.org).
S. Sasikala was with KSR College of arts and science, Tiruchengode, Tamilnadu, India. She is now with the Department of Computer Science (e-mail: email@example.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.