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This paper presents a new approach to two challenging NLP tasks in Classical Tibetan: word segmentation and Part-of-Speech (POS) tagging. We demonstrate how both these problems can be approached in the same way, by generating a memory-based tagger that assigns 1) segmentation tags and 2) POS tags to a test corpus consisting of unsegmented lines of Tibetan characters. We propose a three-stage workflow and evaluate the results of both the segmenting and the POS tagging tasks. We argue that the Memory-Based Tagger (MBT) and the proposed workflow not only provide an adequate solution to these NLP challenges, they are also highly efficient tools for building larger annotated corpora of Tibetan.

Segmenting and POS tagging Classical Tibetan using a memory-based tagger