Natural Language Processing (NLP) is a branch of artificial intelligence focused on enabling computers to understand, interpret, and generate human language. It combines techniques from linguistics, computer science, and machine learning to process both text and speech data. NLP systems analyze language at multiple levels—from individual words and grammatical structures to broader semantic meaning and context—allowing machines to extract meaning from unstructured language data.
Key Tasks
NLP systems perform a range of tasks essential for language understanding. These include tokenization (breaking text into individual words or phrases), part-of-speech tagging, named entity recognition (identifying people, places, and organizations), sentiment analysis, machine translation, and question answering. More complex tasks involve semantic parsing, where systems extract logical meaning from sentences, and coreference resolution, where they identify which pronouns or noun phrases refer to the same entities.
Practical Applications
NLP powers many widely-used technologies. Search engines use NLP to interpret user queries and rank relevant results. Virtual assistants like chatbots rely on NLP to understand user requests and generate appropriate responses. Other applications include machine translation services, email spam filtering, document classification, and information extraction from medical records or legal texts. As machine learning models have become more sophisticated, particularly through deep learning and transformer-based architectures, NLP systems have achieved improved accuracy across these applications.