# Named Entity Recognition Method Based on Multi-Feature Fusion
## 2 Related Work
This model employs Root Mean Square layer Normalization (RMSNorm) and the Switched Gated Linear Unit (SwiGLU) activation function, which substitutes the conventional Layer Normalization (LayerNorm) and the Gaussian error Linear Units (GeLU) and also integrates Bidirectional Long Short-Term Memory (BiLSTM) with Conditional Random Fields (CRF) to bolster performance in NER tasks. The results show that its performance is better than the existing baseline model [10]. Similarly, [PERSON] et al. proposed a model combining character-level CNN and bidirectional LSTM, which significantly improved entity extraction accuracy in NER tasks [11]. While CNN effectively captures local contextual relationships, it reveals limitations in handling long-distance dependencies and lacks sufficient capacity for global semantic understanding. Consequently, the Iterated Dilated Convolutional Neural Network (IDCNN), which offers an expanded receptive field, has emerged as a promising approach in NER tasks. IDCNN is an enhanced convolutional neural network that utilizes dilation to extend its receptive field, effectively handling long-range dependencies while maintaining computational efficiency [12]. Compared to traditional CNNs, IDCNN can capture more distant contextual information without increasing the number of convolutional kernels. [PERSON] et al. demonstrated the application of IDCNN to NER tasks, showing that IDCNN captures local patterns in text efficiently and achieves a much faster processing speed than traditional LSTM-based architectures [13]. [PERSON] et al. proposed a Dilated Gated Convolutional Neural Network (DGCNN) model with residual connections for Chinese NER. This model reduces the risk of gradient vanishing by introducing residual connections, allowing for efficient information transmission across multiple channels. Experimental results on the MSRA and Resume datasets revealed that the model achieved F1 scores of 92.97% and 94.98%, respectively, exhibiting significant advantages in both training speed and performance compared to traditional RNN-based neural networks [14]. BiLSTM and IDCNN each have their strengths and weaknesses. To integrate the advantages of both, [PERSON] et al. proposed a hybrid architecture combining BiLSTM and IDCNN. In this model, IDCNN first performs multi-scale expansion of contextual features, and then, BiLSTM captures global context information. Experimental results demonstrate that this hybrid architecture outperforms several state-of-the-art models for Chinese location name recognition [15]. With the continuous application of named entity recognition, the combination model of the NER model and other models has gradually attracted the attention of scholars. The LDA topic model can effectively identify the research topics in scientific literature. The recurrent neural network can be used to perform named entity recognition on the content of scientific literature and automatically annotate the new theories, new methods, new materials and new tools [16]. For example, [PERSON] et al. used the LDA topic model to perform topic clustering analysis on UGC data of online health communities to extract entity types. Based on the segmented entity types, a BERT-BiLSTM-CRF model was used for named entity recognition. The recognition results are used to construct the knowledge graph. The experimental results show that the combined model is more accurate than the traditional model [17]. In summary, the research method of named entity recognition has experienced from the initial stage based on rules and dictionaries to the stage of machine learning and then to the stage of deep learning as the mainstream. Current research mainly relies on BiLSTMand IDCNN for text feature extraction; however, using a single BiLSTM or IDCNN, or merely combining them sequentially, fails to fully exploit their synergistic potential. Additionally, there is a scarcity of literature on NER for online reviews. To address these limitations, this study proposes a combined NER model that integrates topic analysis.