Low level information extraction a Bayesian network based approach
نویسنده
چکیده
In this article, a contribution is made to information extraction and Bayesian network learning motivated by two practical information extraction tasks. It is shown that some information extraction tasks can be approached as a classification problem where the text is split in tokens and each token is assigned a class. Hidden Markov models are a popular formalism for this task, however they do not deal with tokens having a set of attributes instead of a single one. A new algorithm for this task is presented using various Bayesian networks architectures that deals with multiple attributes per token. Experiments suggest most Bayesian networks architectures perform better than naive Bayes in our problem domain. Hopefully, this article helps in making Bayesian networks more accessible for the information extraction community.
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تاریخ انتشار 2002