用轻量卷积模型实现多语言姓名分类，速度快46倍且省电。
Efficient Multilingual Name Type Classification Using Convolutional Networks
- 多分支深度可分离卷积+分层分类，专为CPU优化
- 104语言4类实体，准确率92.1%，每秒处理2813个名字
- 适合资源有限场景下的高效命名实体识别
我们提出一种基于卷积神经网络的多语言姓名类型分类方法。Onomas-CNN X 模型通过并行卷积分支、深度可分离操作和分层分类机制，在单个CPU核心上实现高效处理。在涵盖104种语言和四类实体（人名、组织、地点、其他）的大规模数据集上，该模型达到92.1%的准确率，每秒处理2813个名字，比微调的XLM-RoBERTa快46倍，同时能耗降低46倍。实验表明，当训练数据充足时，专用卷积架构在特定自然语言处理任务中仍可媲美大型预训练模型。
原文摘要 · Abstract (English)
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.
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