Context Hidden Markov Model for Named Entity Recognition

Branimir T. Todorović (), Svetozar R. Rančić () and Edin H. Mulalić ()
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Branimir T. Todorović: University of Niš
Svetozar R. Rančić: University of Niš
Edin H. Mulalić: Accordia Group LLC

A chapter in Approximation and Computation, 2010, pp 447-460 from Springer

Abstract: Abstract 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.

Keywords: Hide Markov Model; Expectation Maximization Algorithm; Name Entity Recognition; Entity Recognition; Grammar Rule (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-1-4419-6594-3_30

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DOI: 10.1007/978-1-4419-6594-3_30

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