Neural networks in natural language processing (NLP) process text by converting words and sentences into numerical representations, then learning patterns through layers of computation. At a high level, text is first tokenized into smaller units (words, subwords, or characters) and mapped to vectors using embeddings. These vectors capture semantic and syntactic relationships, allowing the network to interpret words based on context. For example, the word “bank” might be represented differently in “river bank” versus “bank account.” The network processes these vectors through hidden layers, which transform the data to identify features like sentence structure or word dependencies. Finally, an output layer generates predictions, such as classifying sentiment or translating a sentence.
Training involves adjusting the network’s parameters to minimize errors. During training, input text is fed forward through the network, and the output is compared to the correct result (e.g., a translation or sentiment label). The difference (loss) is calculated, and backpropagation adjusts the model’s weights to reduce this loss. For instance, in a text classification task, the network might learn that phrases like “great service” correlate with positive sentiment. Over time, the model generalizes these patterns to handle unseen data. Recurrent Neural Networks (RNNs) were early solutions for sequence data, processing text step-by-step while maintaining a hidden state. However, they struggled with long-range dependencies, leading to alternatives like Long Short-Term Memory (LSTM) networks, which use gates to control information flow.
Modern NLP relies heavily on transformer architectures, which use attention mechanisms to weigh the importance of different words in a sequence. Unlike RNNs, transformers process all words in parallel, making them faster and more effective for capturing context. For example, in the sentence “The cat sat on the mat because it was tired,” a transformer’s attention heads might link “it” to “cat” by assigning higher weights to those tokens. Models like BERT (Bidirectional Encoder Representations from Transformers) pre-train on large text corpora to learn universal language representations, which can then be fine-tuned for specific tasks like question answering. Similarly, GPT (Generative Pre-trained Transformer) models generate text autoregressively, predicting the next word based on preceding context. These architectures demonstrate how neural networks balance structural design (e.g., attention layers) with scalable training to solve complex NLP problems efficiently.