Named Entity Recognition Method Based on Multi-Feature Fusion

Nowadays, user-generated content has become a crucial channel for obtaining information and authentic feedback. However, due to the varying cultural and educational levels of online users, the content of online reviews often suffers from inconsistencies in specification and the inclusion of arbitrary information. Consequently, the task of extracting key information from online reviews has become a prominent area of research. This paper proposes a combined entity recognition model for online reviews, aiming to improve the accuracy of Named Entity Recognition (NER). Initially, the Non-negative Matrix Factorization (NMF) model is employed to perform thematic clustering on the review texts, and entity types are extracted based on the clustering results. Subsequently, we introduce an entity recognition model utilizing the pre-trained BERT model as an embedding layer, with BiLSTM and DGCNN incorporating residual connection and gating mechanisms as feature extraction layers. The model also leverages multi-head attention for feature fusion, and the final results are decoded using a Conditional Random Field (CRF) layer. The model achieves an F1 score of 86.8383% on a collected dataset of online reviews containing eight entity categories. Experimental results demonstrate that the proposed model outperforms other mainstream NER models, effectively identifying key entities in online reviews.