  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.
