A Lexical Dependency Probability Model for Mongolian Based On Integration of Morphological and Syntactic Features

Abstract

Syntactic parsing is an important topic in the field of Mongolian language information processing. Compared with English and Chinese dependency parsing, Mongolian dependency parsing is still at the beginning stage. Mongolian syntactic parsing lack of Treebank resources seriously, under such conditions, a high quality syntactic parser cannot be developed by statistical methods simply. Aiming at the characteristics that Mongolian language has rich morphological features, this paper presented a rule and statistics-based dependency parsing model using Mongolian Dependency Treebank as training and evaluation data. The morphological and syntactic rules are represented using complex features and unification operations. The statistical model is represented using lexical dependent probability. This model has now achieved accuracies of 77.18%, 69.42% and 95.44% for the unlabelled annotation score, the labeled annotation score and the head word annotation score respectively.