Tibetan functional chunk recognition using statistical based method

Functional chunk can reveal the skeleton of a sentence and the relation among chunks. Recognizing functional chunk is a sub-field of Natural Language Process, which can effectively improve the performance of syntactic parsing. This paper proposes a Tibetan functional chunk classification. To testify the feasibility ofthe proposed theory, we observe the distribution of Tibetan functional chunks in our corpus. The statistics prove that the classification can describe sentence structure comprehensively. Then we establish a functional chunking model based on a sequencetag model. By introducing appropriate features, a couple of experiments have been conducted. The F1 achieves 82.30 by employing extended features.