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    STUDIA INFORMATICA - Issue no. 1 / 2000  
         
  Article:   TRAINING PROBABILISTIC CONTEXT-FREE GRAMMARS AS HIDDEN MARKOV MODELS.

Authors:  ADRIAN DUDA, GABRIELA ŞERBAN, DOINA TĂTAR. 		 
       
         
  Abstract:  It is considerred in this moment that the use of mathematical statistics methods in natural language processing represents a leading topic in NLP. Statistical methods have first been applied in the "speech-recognition" area. While Hidden Markov Model (HMM) is unanimously accepted as a mathematical tool in this area, its advantages have been less used in dealing with understanding natural language. In this paper we propose a method for association of a HMM to a context-free grammar (CFG). In this way, learning a CFG with a correct parsing tree will be realized by learning a HMM.  
         
     
         
         
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