Explain how you would design a distributed representation-based neural network to leverage the rich similarity space property in your NLP system, addressing the limitations of one-hot encoding. Discuss any potential tradeoffs.

Replace one-hot encoded word vectors with pre-trained word embeddings (e.g., Word2Vec or GloVe). This changes the input representation to capture semantic relationships between words and enables the model to generalize better. However, this may incur increased computational costs during training due to embedding lookups and potentially require more memory for storing large-scale embedding matrices.