The Study of Graininess for Tibetan Named Entity Recognition

Abstract

Tibetan named entity recognition (NER), which is a fundamental part in Tibetan natural language processing, is the important subtask of Information extraction. In this paper, we surveyed the methods, effect and problems of Tibetan NER. And we discussed which kind of tokens that should be taken as the graininess for Tibetan NER task. The paper used two kinds of different graininess in a comparative experiment for Tibetan person names, location names and organization names, based on syllables, or based on words. From the result, we know that the person names based on syllable have better result than that based on words. Location names have small difference while species differ. But the organization names are more suitable based on words.