doi: 10.7763/IJMLC.2012.V2.163
Signet: Web Information Retrieval with NE Disambiguation based on HMM and CRF
S. Balaji1
,
S. Sasikala2,3
- 1Sengunthar Engineering College, Tiruchengode, Tamilnadu, India.
- 2KSR College of arts and science, Tiruchengode, Tamilnadu, India
- 3Department of Computer Science.
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.
Keywords
- HMM
- HCRF
- Named-Entity
- SIGNET
How to Cite
S. Balaji and S. Sasikala, "Signet: Web Information Retrieval with NE Disambiguation based on HMM and CRF," International Journal of Machine Learning, vol. 2, no. 4, pp. 443-445, 2012. https://doi.org/10.7763/IJMLC.2012.V2.163
Copyright & License
Copyright © 2012 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).