Trie-Augmented Neural Networks (TANNs) combine trie structures with neural
networks, forming a hierarchical design that enhances decision-making
transparency and efficiency in machine learning. This paper investigates the
use of TANNs for text and document classification, applying Recurrent Neural
Networks (RNNs) and Feed forward Neural Networks (FNNs). We evaluated TANNs on
the 20 NewsGroup and SMS Spam Collection datasets, comparing their performance
with traditional RNN and FFN Networks with and without dropout regularization.
The results show that TANNs achieve similar or slightly better performance in
text classification. The primary advantage of TANNs is their structured
decision-making process, which improves interpretability. We discuss
implementation challenges and practical limitations. Future work will aim to
refine the TANNs architecture for more complex classification tasks.

TANNs 结合字典树结构和神经网络，提供了一种层次化设计，能够增强机器学习中的决策透明度和效率。本研究探讨了在文本和文档分类中应用 TANNs 的方法，使用了循环神经网络（RNNs）和前馈神经网络（FNNs）。通过对比传统的 RNN 和 FFN 网络，包括有无 dropout 正则化，我们在 20 NewsGroup 和 SMS 垃圾短信收集数据集上评估了 TANNs 的性能。结果表明，在文本分类方面，TANNs 的性能相当或稍微优于传统方法。TANNs 的主要优点在于其结构化的决策过程，从而提高了可解释性。我们还讨论了实现上的挑战和实际限制。未来工作将致力于改进 TANNs 架构以应对更复杂的分类任务。