A hybrid GRU-CRF framework for enhancing English tokenization efficiency and accuracy

Natural Language Processing (NLP) has evolved from tasks like language structure analysis, machine translation, and speech recognition to more complex applications such as intelligent dialogue systems and social media analysis. However, traditional tokenization methods, which rely heavily on dictionaries and rule-based approaches, often yield inconsistent results due to a lack of standardization and the dependency on manual annotations. While deep learning methods offer improved solutions, they typically require significant computational resources and are challenging to adapt. This paper proposes a hybrid model that combines Gated Recurrent Units (GRUs) for efficient feature extraction and Conditional Random Fields (CRFs) for robust sequence labeling, aimed at enhancing the efficiency and accuracy of English tokenization. The proposed GRU-CRF framework is evaluated on the Stanford Question Answering Dataset (SQuAD), demonstrating superior performance over traditional models such as LSTM and Bi-LSTM. The results show that our hybrid model achieves higher accuracy and greater efficiency, making it a promising approach for advancing tokenization tasks in NLP. This study highlights the potential of integrating multimodal feature fusion and hybrid architectures in optimizing NLP applications.