Research on expert information extraction based on Word2Vec and improved Transformer

In response to issues such as high-dimensional sparsity, missing semantic information, and ambiguous topic boundaries in traditional methods, in this paper, we investigated a document expert information extraction method based on Word2Vec and Transformer, aiming to enhance the semantic accuracy and clustering effectiveness of document expert information extraction. First, semantic embedding vectors were generated through the Word2Vec model, effectively reducing high-dimensional sparsity and enhancing the semantic representation capability of documents. Second, the Transformer algorithm was used to extract expert information from document vectors, achieving effective differentiation between topics. Experiments showed that document semantic embedding based on Word2Vec can significantly improve the performance of Transformer in expert information extraction. Compared to the traditional TF-IDF + Transformer method, this approach demonstrates superior performance in topic consistency and semantic capture.