北京大学学报（自然科学版）2025，Vol.61Issue(3)：440-450,11.DOI:10.13209/j.0479-8023.2025.038
基于图解析的端到端片段藏文语义角色标注方法
End-to-End Spanning Tibetan Semantic Role Labeling Based on Graph Parsing
摘要
Abstract
Semantic role labeling,as an essential pathway to semantic understanding,has a wide range of appli-cations in machine translation,information extraction,and question and answer systems.This paper proposes a graph parsing-based end-to-end spanning semantic role labeling method for Tibetan,based on existing Tibetan semantic labeling systems and methods,by referring to the more mature semantic role labeling methods in English and Chinese.The method converts span-based semantic role labeling in Tibetan into a word-based graph parsing task,and the process is divided into two phases:semantic role labeling to graph conversion and graph to semantic role labeling recovery.In the first stage,a Tibetan pre-training language model(TiUniLM)is used for dynamic word embedding,and predicates are automatically specified by introducing the predicate indicator P.Then,temporal features are further modeled by designing a"gating"mechanism long short-term memory network(GM-LSTM).The second stage uses Viterbi constraint decoding to correct the illegitimate graphs.Experiments on TSRLD-Span show that the proposed method can achieve the best F1 value of 89.69%on the test set,which is a significant improvement in performance compared with the baseline model,indicating that the method is effective.关键词
自然语言处理/图解析/片段/藏文语义角色标注/谓词标识器Key words
natural language processing(NLP)/graph parsing/span/Tibetan semantic role labeling/predicate indicator引用本文复制引用
班玛宝,罗鹏,头旦才让,尼玛扎西,才让加,于永斌..基于图解析的端到端片段藏文语义角色标注方法[J].北京大学学报（自然科学版）,2025,61(3):440-450,11.基金项目
四川省自然科学基金青年基金(25QNJJ3501)、藏语智能全国重点实验室开放课题(2024-Z-001)、科技创新2030—"新一代人工智能"重大项目(2022ZD0116100)和国家自然科学基金(62306158)资助 （25QNJJ3501）
