Uninterpretability has become the biggest obstacle to the wider application of deep neural network, especially in most human–machine interaction scenes. Inspired by the powerful associative computing ability of human brain neural system, a novel interpretable semantic representation model of noun context, associative knowledge network model, is proposed. The proposed network structure is composed of only pure associative relationships without relation label and is dynamically generated by analysing neighbour relationships between noun words in text, in which incremental updating and reduction reconstruction strategies can be naturally introduced. Furthermore, a novel interpretable method is designed for the practical problem of checking the semantic coherence of noun context. In proposed method, the associative knowledge network learned from the text corpus is first regarded as a background knowledge network, and then the multilevel contextual associative coupling degree features of noun words in given detection document are computed. Finally, contextual coherence detection and the location of those inconsistent noun words can be realized by using an interpretable classification method such as decision tree. Our sufficient experimental results show that above proposed method can obtain excellent performance and completely reach or even partially exceed the performance obtained by the latest deep neural network methods especially in F1 score metric. In addition, the natural interpretability and incremental learning ability of our proposed method should be extremely valuable than deep neural network methods. So, this study provides a very enlightening idea for developing interpretable machine learning methods, especially for the tasks of text semantic representation and writing error detection.

不可解释性已成为深度神经网络更广泛应用的最大障碍，尤其是在大多数人机交互场景中。受人脑神经系统强大的联想计算能力的启发，提出了一种新颖的名词上下文可解释语义表示模型——联想知识网络模型。所提出的网络结构仅由纯关联关系组成，没有关系标签，通过分析文本中名词词之间的相邻关系动态生成，其中可以自然地引入增量更新和减少重建策略。此外，针对检查名词上下文语义连贯性的实际问题，设计了一种新的可解释方法。在提出的方法中，从文本语料库中学习到的关联知识网络首先被视为背景知识网络，然后计算给定检测文档中名词词的多级上下文关联耦合度特征。最后，可以使用决策树等可解释的分类方法来实现上下文连贯性检测和那些不一致的名词词的定位。我们充分的实验结果表明，上述方法可以获得优异的性能，完全达到甚至部分超过最新的深度神经网络方法获得的性能，尤其是在 F1 分数度量上。此外，我们提出的方法的自然可解释性和增量学习能力应该比深度神经网络方法更有价值。所以，