Context is crucial in NLP because the meaning of words or phrases often depends on their surrounding text. For example, the word "bank" can mean a financial institution or the edge of a river, depending on its context. Without understanding context, NLP models struggle with ambiguity, idioms, and polysemy.
Modern NLP models like BERT and GPT use contextual embeddings, which generate representations for words based on their position and usage within a sentence. This allows them to differentiate meanings dynamically, such as interpreting "He deposited money in the bank" differently from "He sat by the bank of the river." Context also plays a vital role in tasks like machine translation, where preserving sentence-level meaning is essential.
Capturing context improves the accuracy and relevance of NLP outputs, particularly in applications like question answering, summarization, and conversational AI. Advances in self-attention mechanisms and pre-trained language models have significantly enhanced the ability of NLP systems to process and retain context effectively.