词嵌入模型研究综述OA

As a foundational technology in natural language processing,word embedding models map discrete linguistic symbols into continuous vector representations that computers can process.Their representational capacity and generalization performance directly impact the effectiveness of downstream tasks.Traditional word representation methods struggle to capture semantic relationships between words,while static word embedding methods fail to handle polysemy effectively.With the development of pre-trained language models and large language models,word embedding technology has gradually evolved from fixed vector representations to dynamic representations with contextual awareness.However,current research has yet to systematically incorporate the new paradigms of embedding models in the era of large language models.This paper comprehensively reviews the development trajectory of word embedding models,dividing their evolution into four stages based on typical technical paradigms:static word embeddings based on statistics,static word embeddings based on feedforward neural networks,dynamic word embeddings based on pre-trained language models,and dynamic word embeddings based on large language models.For each stage,the representative models,core principles,and their advan-tages and disadvantages are elaborated.The dynamic word embedding methods based on large language models are systematically categorized into three technical approaches:pooling,prompt engineering,and fine-tuning.The intrinsic and extrinsic evaluation methods for word embedding models and their interrelationships are examined.Addressing the current limitations of word embedding models,future research directions such as multimodal fusion,efficiency optimization,low-resource adaptation,and interpretability are proposed.