Interpolated probabilistic tagging model optimized with genetic algorithm
Derek F. Wong, S. Chao, Dongcheng Hu, Yu-Hang Mao · 2005
We present results of probabilistic tagging of Portuguese texts in order to show how these techniques work for one of the highly morphologically ambiguous inflective languages by using a limited corpus as the basic training source. In order to cope the ambiguities problem caused by the insufficient training data, especially the unknown words, we incorporate the lexical features into the probabilistic model. Different from other proposed tagging models, these features are introduced into the word probabilities by means of interpolation. A technique to determine the optimal set of interpolation parameters based on genetic algorithm is described. Our preliminary result shows that we can correctly tag 91.8% of the sentences based on our tagging model.