Context Hidden Markov Model for Named Entity Recognition
Named entity (NE) recognition is a core technology for understanding low-level semantics of texts. In this paper we consider the combination of two classifiers: our version of probabilistic supervised machine learning classifier, which we named the Context Hidden Markov Model, and grammar rule-based system in named entity recognition. In order to deal with the problem of estimating the probabilities of unseen events, we have applied the probability mixture models which were estimated using another machine learning algorithm: Expectation Maximization. We have tested our Named Entity Recognition system on MUC 7 corpus.
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