An Efficient Corpus Based Part-of-Speech Tagging with GEP

Text corpora which are tagged with part-of-speech (pos) information are useful in many areas of linguistic research. This paper proposes a model of Genetic Expression Programming (GEP) for pos tagging. GEP is used to search for appropriate structures in function space. After the evolution of sequence of tags, GEP can find the best individual as solution. Before simulation, a set of appropriate parameters of algorithm is fitted. Experiments on Brown Corpus show that the proposed model can achieve higher accuracy rate than Genetic Algorithm model and HMM model.