Abstract: Named Entity Recognition (NER) aims to accurately identify entities with predefined semantic categories and clear boundaries from text. In Chinese NER, the absence of explicit word boundaries, the complexity of semantic expressions, and the widespread presence of polyphonic and visually similar characters often lead to semantic ambiguity. Existing methods predominantly rely on character- or word-level information, with insufficient utilization of key linguistic features such as pinyin and radicals, and multi-source heterogeneous feature fusion is typically performed via simple concatenation or weighting strategies, which fail to capture deep semantic correlations among different features and thus limit further performance improvements. To address these issues, this paper proposes a Chinese NER method based on Multi-Feature Hierarchical Fusion (MFHF) to achieve collaborative modeling and deep semantic integration of multi-dimensional linguistic features. Specifically, in the feature representation stage, four types of embeddings—character, pinyin, radical, and lexical—are constructed, where character embeddings are derived from a pre-trained language model to capture contextual semantic information and long-range dependencies, pinyin embeddings encode phonetic sequences to model pronunciation differences and alleviate polyphonic ambiguity, radical embeddings employ a convolutional neural network to model character structures and extract fine-grained semantic features from the glyph level, and lexical embeddings incorporate word-level information via a lexicon matching mechanism to enhance the model’s ability to detect multi-character entity boundaries, thereby improving character representations from phonetic, glyph, and lexical semantic perspectives. To address insufficient interaction and coarse granularity in multi-source feature fusion, a hierarchical cross-attention mechanism is designed, where at the local level, two groups of cross-attention—pinyin–radical and character–lexical—are constructed to model the intrinsic relationships between phonetic and glyph information as well as the structural dependencies between character-level and word-level semantics through bidirectional attention interactions, enabling fine-grained alignment and complementarity among heterogeneous features, and at the global level, the locally enhanced multi-source features are concatenated and further modeled using a multi-head self-attention mechanism to capture long-range dependencies across features, achieving deep semantic integration and generating semantically enriched representations. On this basis, a joint optimization strategy combining multi-task learning and adversarial training is introduced, where auxiliary tasks of pinyin prediction and radical prediction are designed to strengthen feature learning, and gradient-based adversarial perturbations are applied in the embedding space to improve robustness and generalization under complex conditions. Finally, the fused representations are fed into a Bidirectional Long Short-Term Memory (BiLSTM) network for sequence modeling, and a Conditional Random Field (CRF) layer is employed for global decoding to obtain entity recognition results. Experiments conducted on three public Chinese NER datasets, MSRA, Weibo, and Resume, demonstrate that the MFHF model achieves F1 scores of 96.78%, 96.14%, and 71.80%, respectively, outperforming several representative baseline models, with improvements of 1.09, 1.55, and 1.68 percentage points over CPL-NER, GS-Lexicon, and Lattice-LSTM on the respective datasets. In summary, the proposed approach effectively enhances semantic modeling capability and model robustness for Chinese NER through multi-feature hierarchical fusion and joint optimization strategies.

摘要： 命名实体识别旨在从文本中准确识别具有特定语义类别及明确边界的实体。针对中文文本中缺乏显式词边界、语义表达复杂以及多音字、形近字广泛存在所带来的语义歧义问题，现有方法多依赖字符或词汇信息进行建模，对拼音、部首等关键语言学特征的利用仍显不足，同时在多源异构特征融合过程中多采用简单拼接或加权方式，难以充分挖掘不同特征之间的深层语义关联，进而限制了模型整体性能的进一步提升。本文提出一种基于多特征层次化融合（Multi-Feature Hierarchical Fusion，MFHF）的中文命名实体识别方法，以实现多维语言学特征的协同建模与深度语义融合。首先，在特征表示阶段构建字符、拼音、部首和词汇四类嵌入表示，其中字符嵌入基于预训练语言模型获取上下文语义信息并捕获长距离依赖关系，拼音嵌入通过编码拼音序列刻画语音差异，有助于缓解多音字歧义问题，部首嵌入利用卷积神经网络对汉字结构进行建模，从字形层面提取细粒度语义特征，词汇嵌入基于词典匹配机制引入词级信息，以增强模型对多字实体边界的感知能力，从而从语音、字形和词汇语义多个维度提升字符表示能力。其次，为解决多源特征融合过程中信息交互不足及融合粒度单一问题，设计层次化交叉注意力机制，在局部层面构建拼音—部首与字符—词汇两组交叉注意力，通过双向注意力交互分别建模音形之间的内在关联以及字级与词级语义之间的结构关系，使不同模态特征能够在细粒度层面实现信息对齐与互补，在全局层面，将经过局部增强的多源特征进行拼接，并引入多头自注意力机制对其进行统一建模，实现跨特征的深层语义整合，从而获得兼具多维信息的语义增强表示。在此基础上，引入多任务学习与对抗训练的联合优化策略，通过拼音预测和部首预测辅助任务强化特征学习，并在嵌入空间加入基于梯度的对抗扰动，以提升模型在复杂环境下的鲁棒性与泛化能力。最后，将融合后的特征表示输入BiLSTM进行序列建模，并通过条件随机场进行全局解码，从而获得实体识别结果。为验证所提方法的有效性，在MSRA、Weibo和Resume三个公开中文命名实体识别数据集上进行实验评估，并从整体性能与不同实体类型两个层面进行对比分析。实验结果表明，MFHF模型在MSRA、Resume和Weibo数据集上分别取得96.78%、96.14%和71.80%的F1值，整体性能优于多种代表性基线模型。其中，在MSRA数据集上相较于CPL-NER模型取得1.09个百分点的性能提升，在Weibo数据集上较GS-Lexicon模型提升1.55个百分点，在Resume数据集上相较于Lattice-LSTM模型取得1.68个百分点的提升。综上，本文方法通过多特征层次化融合与联合优化策略，有效提升了中文命名实体识别的语义建模能力与模型鲁棒性。