Natural language processing
Natural language processing
Natural language processing (NLP) is a subfield of artificial intelligence concerned with the interaction between computers and human language. The goal of NLP is to enable machines to understand, interpret, and generate human language in a way that is both meaningful and useful. Applications range from machine translation and sentiment analysis to chatbots and voice assistants.
The field draws on computational linguistics, computer science, and statistical modeling to process and analyze large amounts of natural language data. Early approaches relied on hand-crafted rules and symbolic AI, but modern NLP is dominated by deep learning and neural networks, particularly transformer models such as BERT and GPT.
History
NLP research began in the 1950s, notably with the Georgetown–IBM experiment in 1954, which demonstrated automatic translation of Russian sentences into English. During the 1960s and 1970s, researchers developed rule-based systems for syntax and semantics, such as SHRDLU and ELIZA. The 1980s saw the rise of statistical NLP, using corpora and machine learning algorithms.
The 2010s brought a revolution with the introduction of word embeddings (e.g., word2vec, GloVe) and later attention mechanisms. The release of the Transformer architecture in 2017 led to pretrained language models like GPT-2, BERT, and GPT-3, dramatically improving performance on tasks such as question answering and text generation.
Features
- 0 : Splitting text into words, subwords, or characters.
- 0 : Assigning grammatical labels (noun, verb, etc.) to tokens.
- 0 : Identifying entities like people, organizations, and locations.
- 0 : Determining the emotional tone of text.
- 0 : Translating text between languages.
- 0 : Generating concise summaries of longer documents.
- 0 : Providing answers to questions posed in natural language.
- 0 and text-to-speech: Converting between spoken and written language.