NLP

NLP, short for natural language processing, is a field at the intersection of computer science, artificial intelligence, and linguistics that studies interactions between computers and human language. Its purpose is to enable computers to understand, interpret, and generate human language in ways that are useful for people and systems.

Core tasks include tokenization, part-of-speech tagging, syntactic parsing, named entity recognition, coreference resolution, sentiment analysis, machine translation, summarization, question answering, and dialog systems. Approaches range from rule-based linguistics to statistical methods and, increasingly, data-driven neural networks; modern NLP emphasizes large-scale deep learning models.

NLP relies on text corpora and annotated data for training and evaluation. Common automatic metrics include BLEU, ROUGE, METEOR, perplexity, and F1, complemented by human judgments in many applications. Shared tasks and benchmarks, such as translation, QA, and language-understanding challenges, guide progress.

History: early NLP relied on hand-crafted rules and expert systems. In the 1990s, statistical methods became dominant. Since the rise of deep learning in the 2010s, neural networks and transformer architectures have driven major advances in translation, comprehension, and language generation. Notable developments include RNNs, LSTMs, and transformers, with models such as BERT, GPT, and T5 illustrating the value of pretraining and fine-tuning.

Applications span search engines, virtual assistants, machine translation, document summarization, sentiment analysis, and information extraction. Challenges include linguistic ambiguity, variability across languages, low-resource languages, data bias and fairness, privacy concerns, and robustness to noise and adversarial inputs.