# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
## 2 Related Work
Pointer networks can generate variable-length output sequences, breaking the limitation of fixed sequence length in general sequence-to-sequence models. In entity recognition models based on pointer networks, the text is used as input data. The model first predicts the start and end boundaries of entities to obtain entity spans, and then computes the representation of the entity spans to predict the entity types corresponding to the entity boundaries. [PERSON] et al. (2023) introduced pointer networks in their model to perform sequence chunking and labeling. [PERSON] et al. (2024) used GRU neural networks at the feature extraction layer to extract semantic features of the data and used pointer networks to eliminate the ambiguity of entity boundaries. Pointer networks have shown significant effectiveness in handling nested entities. However, in general, pointer networks treat multi-entity extraction tasks as multiple binary classification problems, which may lead to slow convergence when dealing with long sequences. Additionally, adding effective features or incorporating multi-task learning mechanisms can improve the recognition ability of span-based models. [PERSON] et al. (2025) first extract all possible entity boundaries and then perform boundary detection and entity classification simultaneously. [PERSON] et al. (2026) utilize a pre-trained Transformer model to jointly perform entity recognition and relation extraction in the span-based approach, and experiments show that pre-trained Transformer modules can enhance model performance. [PERSON] et al. (2027) proposes a three-affine attention mechanism that incorporates label information into the span-based approach, using the three-affine mechanism to calculate entity scores for recognizing nested entities. This model interacts with both the span boundaries and the entity boundaries, and extends the two-affine mechanism with an additional dimension to obtain the three-affine mechanism. The span boundaries and the embeddings of the spans are used as the query vectors \(Q\), while the word embeddings of each word within the span are used as the key vectors \(K\) and value vectors \(V\) in the attention mechanism. Compared to the general attention mechanism, this approach facilitates higher-dimensional interactions between \(Q\) and \(K\). Although this method handles the internal information within spans, the three-affine mechanism introduces an additional dimension compared to the two-affine mechanism, which may result in increased computational overhead when processing entity classification. In summary, the previous two methods, namely the sequence labeling-based method and the hypergraph-based method, are not sensitive to entity boundary information. These methods may exhibit boundary ambiguity when dealing with nested entities, which affects the extraction of entity boundaries. To address these issues, this paper adopts the span-based method, which processes each potential entity boundary to extract all nested entities. [PERSON] et al. (2028) proposed a nested entity recognition module called GlobalPointer in 2022. This module enumerates all potential entity boundaries of the text using upper triangular matrices. Each category has such an upper triangular matrix. The module calculates these matrices to obtain scores for each entity boundary in each category, thereby recognizing all nested entities in the text. However, a single GlobalPointer module may struggle to extract sufficient semantic information to distinguish similar categories in fine -grained entity recognition datasets. Therefore, this paper constructs a deep semantic extraction module called ATT-IDCNNLR-ATTR, which leverages multiple layers of convolution, residual structures, and attention mechanisms to handle complex semantic information in the text and enable the model to extract more semantic information.