Tibetan Named Entity Recognition Based on Small Sample Learning

Abstract

Abstract: The task of Tibetan named entity recognition is to identify the names of people， places and organizations in the text. This paper proposed a Tibetan named entity recognition method based on small sample learning. In the training process， the feature fusion of entity location information， word segmentation information and Tibetan syllable information in the form of dimensional splicing could better represent the boundary information of Tibetan long entities. Ablation experiments were designed to explore the effect of different feature information on model performance. The experimental results show that our method is effective， and the F1 value is improved by 22.22~38 percentage points compared with the baseline experiment.

Key words: small sample learning, Tibetan, named entity recognition, entity feature information fusion

YU Tao, ZHANG Ying, Yong Tso, . Tibetan Named Entity Recognition Based on Small Sample Learning[J]. Computer and Modernization, 2023, 0(05): 13-19.

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