A hybrid approach to Lao word segmentation using longest syllable level matching with named entities recognition

The Lao language is written without words delimiter which makes it extremely difficult to process. The development of automatic word segmentation for natural language processing for the Lao language is an essential but challenging task. This paper proposes a longest syllable level match with named entities recognition approach for Lao word segmentation. Syllables were first extracted from the input text and then longest matching was applied. This is one of the techniques in the Dictionary Based approach with named entities recognition being used to combine them to form the words. The performance result obtained from this approach, in precision and recall, was 85.21% and 92.36%, respectively.