Soft Computing: Collective information extraction

This book introduces Relational Markov Networks (RMN's) for Collective Information Extraction. Also, an attempt has been made to improve the performance of Relational Markov Network using Approximate Inference Procedure such as Gibbs Sampling for Collective Information Extraction. Gibbs Sampling has been used for making the performance of LT-RMN's better than CRF's for Collective Information Extraction.

Autorentext

Dr. Gagandeep received his Bachelor's degree in Computer Science and Engineering from Punjab Technical University, Jalandhar, Punjab, India in 2002, M.E. degree in Computer Science and Engineering from PEC University of Technology, Chandigarh, India, in 2005 and Ph.D. degree in Computer Engineering from Panjabi university, Patiala, India, in 2017.

Klappentext

This book introduces Relational Markov Networks (RMN's) for Collective Information Extraction. Also, an attempt has been made to improve the performance of Relational Markov Network using Approximate Inference Procedure such as Gibbs Sampling for Collective Information Extraction. Gibbs Sampling has been used for making the performance of LT-RMN's better than CRF's for Collective Information Extraction.