Hidden Markov Models for Information Extraction
نویسنده
چکیده
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.
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تاریخ انتشار 2001