Abstract
Modern Tibetan and Vertical (Traditional) Mongolian are scripts used by c.11m people, mostly within the People’s Republic of China. In terms of publicly available tools for NLP, these languages and their scripts are extremely low-resourced and under-researched. We set out firstly to survey the state of NLP for these languages, and secondly to facilitate research by historians and policy analysts working on Tibetan newspapers. Their primary need is to be able to carry out Named Entity Recognition (NER) in Modern Tibetan, a script which has no word or sentence boundaries and for which no segmenters have been developed. Working on LightTag, an online tagger using character-based modelling, we were able to produce gold-standard training data for NER for use with Modern Tibetan.
