On smoothing techniques for bigram-based natural language modelling

The authors study various problems related to smoothing bigram probabilities for natural language modeling: the type of interpolation, i.e. linear vs. nonlinear, the optimal estimation of interpolation parameters, and the use of word equivalence classes (parts of speech). A nonlinear interpolation method that results in significant improvements over linear interpolation in the experimental tests is proposed. It is shown that the leaving-one-out method in combination with the maximum likelihood criterion can be efficiently used for the optimal estimation of interpolation parameters. In addition, an automatic clustering procedure is developed for finding word equivalence classes using a maximum likelihood criterion. Experimental results are presented for two text databases: a German database with 100000 words and an English database with 1.1 million words