Graphic language model for agglutinative languages: Uyghur as study case

Abstract

This paper describes a novel, graphic language modeling strategy for morphologically rich agglutinative languages. Different from the linear structure in n-gram language models, graphic modeling organizes the morphemes in a sentence, including stems and affixes, as a directed graph. The graphic language model is verified in two typical application scenarios, morphological analysis and machine translation. We take Uyghur for example, and experiments show that the graphic language model achieves significant improvement in both morphological analysis and machine translation. © Springer-Verlag 2013.