Abstract: Tibetan named entity recognition faces several practical challenges,including scarce annotated data,unreliable tokenization,and degraded performance on long entities.To address these issues,this paper proposes a NER framework based on byte-level global modeling and a gated feature fusion mechanism.Specifically,the framework employs the Byte Latent Transformer to directly model raw UTF-8 byte sequences,and leverages its entropy-based dynamic patching mechanism to obtain global contextual representations without relying on a fixed vocabulary.This effectively avoids entity boundary corruption introduced by conventional tokenizers and improves robustness to out-of-vocabulary patterns.Meanwhile,a gated linear conditional fusion module is designed to inject aligned global contextual features into local boundary-preserving representations with controllable strength in an adaptive,position-wise manner,thereby forming complementary features and enhancing span modeling capability.On this basis,the model combines BiLSTM and CRF to perform sequence modeling and label decoding,jointly preserving global semantic modeling and local boundary discrimination.Experimental results on the TibetanAI_NER and TibNER datasets show that the proposed model improves the F1 score by 11.5 and 4.53 percentage points,respectively,over the corresponding state-of-the-art baselines.Ablation studies further verify the synergistic contributions of the global features and the gated fusion mechanism.Overall,the proposed framework consistently strengthens entity boundary discrimination under segmentation-constrained and low-resource scenarios,with particularly clear gains on long entity recognition,providing a feasible modeling approach for NER in low-resource languages.

Key words: Tibetan named entity recognition, Byte-level modeling, Gated feature fusion, Hybrid granularity strategy, Boundary-aware