Neural networks work in NLP by modeling relationships between words, sentences, and documents to perform tasks like sentiment analysis, translation, and summarization. Word embeddings, such as Word2Vec or GloVe, transform text into numerical vectors that capture semantic meaning, serving as inputs for neural models.
Recurrent Neural Networks (RNNs) and their variants like LSTMs and GRUs excel in handling sequential text data by preserving context across long sentences. More recently, Transformer models, such as BERT and GPT, have revolutionized NLP by using self-attention mechanisms to process entire sentences or paragraphs simultaneously, capturing both global and local dependencies.
Pretrained language models are fine-tuned on specific NLP tasks using task-specific datasets, making them versatile for a wide range of applications. For instance, BERT can be fine-tuned for text classification, while GPT models are often used for text generation and conversational AI. Neural networks have dramatically improved NLP by enabling more accurate, context-aware, and scalable solutions.