Word Segmentation for Burmese Based on Dual-Layer CRFs

Abstract

Burmese is an isolated language, in which the syllable is the smallest unit. Syllable segmentation methods based on matching lead to performance subject to the syllable segmentation effect. This article proposes a word segmentation method with fusion conditions of double syllable features. It combines word segmentation and segmentation of syllables into one process, thus reducing the impact of errors on the syllable segmentation of Burmese. In the first layer of the conditional random fields (CRF) model, Burmese characters as atomic features are integrated into the Burma section of the Barkis Speech Paradigm (Backus normal form) features to realize the Burma syllable sequence tags. In the second layer of the CRFs model, with the syllable marked as input, it realizes the sequence markers through building a feature template with syllables as atomic features. The experimental results show that the proposed method has a better effect compared with the method based on the matching of syllables.