基于藏文音节结合BiLSTM-CRF的藏语语义组块分类标注

Tibetan Semantic Chunking Classification and Labeling based on Tibetan Syllables and BiLSTM-CRF

针对藏语句子语义分析中语义种类繁多且广泛存在歧义的难点,提出了基于藏文音节向量和BiL-STM-CRF混合模型相结合的藏语语义组块识别方法。首先制定了13种语义组块标注规范,其次构建了13211句语义组块标注语料库,在此基础上采用TS-BiLSTM-CRF方法训练了藏语语义组块识别和分类模型。综合测试实验结果表明,该模型精确率为75.03%,召回率为76.52%,F1值为75.77%。各类语义组块识别中,指示类(INS)识别的测评结果远高于其他几类语义组块,精确率为90.87%;组织类(ORG)的测评结果偏低于其他类型,精确率为66.67%。文章研究证实了TS-BiLSTM-CRF模型在藏语语义组块识别分析任务中具有较好的性能。

A Tibetan semantic chunking recognition method based on the combination of Tibetan syllable vectors and BiLSTM-CRF hybrid model is proposed to address the difficulties associated with diverse semantic types and ambiguities in the semantic analysis of Tibetan sentences.Firstly,13 semantic chunking annotation standards were developed,and a semantic chunking annotation corpus comprising 13211 sentences was then constructed.Based on this,the Tibetan semantic chunking recognition and classification model was trained using the TS-BiLSTM-CRF method.The results of the comprehensive test experiment show that the accuracy rate,the recall rate,and the F1 value are 75.03%,76.52%,and 75.77%,respectively.Among all types of semantic chunking recognition,the evaluation results show that the accuracy rate of INS class recognition are much higher compared to other types of semantic blocks,with a value of 90.87%,while the ORG class has a lower accuracy rate of 66.67%than those of other types.This study validates that the TS-BiLSTM-CRF model exhibits strong performance in Tibetan semantic chunking recognition and analysis tasks.