Research on Tibetan Semantic Role Labeling using an Integrated Strategy

Abstract

Semantic role labeling is one of the most significant research fields of natural language processing. Researchers have already made many achievements in English and Chinese semantic role labeling. Until now, however, Tibetan semantic role labeling is still at an early stage due to the lack of a Tibetan corpus with semantic role annotation and relatively outdated research approaches. Tibetan is rich with syntactic markers that naturally divide a sentence into semantic chunks and indicate the semantic relationships between these chunks. Thus, in this paper, we propose a semantic role classification and an integrated strategy for Tibetan semantic role labeling. Transformation-Based Error-driven Learning and Conditional Random Fields have been employed in our study. Additionally, a number of linguistic rules have been introduced into our approach as well. Our integrated strategy achieves 83.91% in precision, 82.78% in recall, and an F-score of 85.71.