We present a convolutional neural network approach for classifying proper names by language and entity type. Our model, Onomas-CNN X, combines parallel convolution branches with depthwise-separable operations and hierarchical classification to process names efficiently on CPU hardware. We evaluate the architecture on a large multilingual dataset covering 104 languages and four entity types (person, organization, location, other). Onomas-CNN X achieves 92.1% accuracy while processing 2,813 names per second on a single CPU core - 46 times faster than fine-tuned XLM-RoBERTa with comparable accuracy. The model reduces energy consumption by a factor of 46 compared to transformer baselines. Our experiments demonstrate that specialized CNN architectures remain competitive with large pre-trained models for focused NLP tasks when sufficient training data exists.

翻译：本文提出一种基于卷积神经网络的专有名词语言与实体类型分类方法。我们的模型Onomas-CNN X通过并行卷积分支与深度可分离操作相结合，并采用分层分类机制，实现在CPU硬件上高效处理名称数据。我们在覆盖104种语言和四种实体类型（人物、组织、地点、其他）的大规模多语言数据集上评估该架构。Onomas-CNN X在单CPU核心上以每秒处理2,813个名称的速度达到92.1%的准确率——其处理速度比经过微调的XLM-RoBERTa模型快46倍，且准确率相当。与基于Transformer的基线模型相比，本模型将能耗降低了46倍。实验结果表明，在具备充足训练数据的情况下，针对特定自然语言处理任务设计的专用CNN架构仍能与大型预训练模型保持竞争力。