Deep learning has advanced NLP, but interpretability remains limited, especially in healthcare and finance. Concept bottleneck models tie predictions to human concepts in vision, but NLP versions either use binary activations that harm text representations or latent concepts that weaken semantics, and they rarely model dynamic concept interactions such as negation and context. We introduce the Concept Language Model Network (CLMN), a neural-symbolic framework that keeps both performance and interpretability. CLMN represents concepts as continuous, human-readable embeddings and applies fuzzy-logic reasoning to learn adaptive interaction rules that state how concepts affect each other and the final decision. The model augments original text features with concept-aware representations and automatically induces interpretable logic rules. Across multiple datasets and pre-trained language models, CLMN achieves higher accuracy than existing concept-based methods while improving explanation quality. These results show that integrating neural representations with symbolic reasoning in a unified concept space can yield practical, transparent NLP systems.

翻译：深度学习推动了自然语言处理的发展，但其可解释性仍然有限，尤其是在医疗和金融领域。概念瓶颈模型将预测与视觉中的人类概念相关联，但自然语言处理版本要么使用二元激活损害文本表示，要么使用削弱语义的潜在概念，并且很少建模动态概念交互，例如否定和上下文。我们引入了概念语言模型网络（CLMN），这是一种神经符号框架，既能保持性能，又能保持可解释性。CLMN将概念表示为连续、人类可读的嵌入，并应用模糊逻辑推理来学习自适应交互规则，这些规则陈述概念如何相互影响并影响最终决策。该模型通过概念感知表示增强原始文本特征，并自动归纳出可解释的逻辑规则。在多个数据集和预训练语言模型上，CLMN在提高解释质量的同时，实现了比现有基于概念的方法更高的准确率。这些结果表明，在统一的概念空间中整合神经表示与符号推理可以产生实用、透明的自然语言处理系统。