Hidden Markov Models for Information Extraction
Nancy R. Zhang · 2001
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 stochastic modeling tasks. This paper uses a machine learning approach to examine the effectiveness of HMMs on extracting information of varying levels of structure. A stochastic optimization procedure is used to find the optimal structure for a given task, and a modified version of the Baum Welch algorithm is used for parameter estimation. I.