Approximation and Computation

Volume 42 of the series Springer Optimization and Its Applications pp 447-460


Context Hidden Markov Model for Named Entity Recognition

  • Branimir T. TodorovićAffiliated withFaculty of Science and Mathematics, University of Niš Email author 
  • , Svetozar R. RančićAffiliated withFaculty of Science and Mathematics, University of Niš
  • , Edin H. MulalićAffiliated withAccordia Group LLC

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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.