Overview
Natural language processing connects linguistic structure with statistical and neural computation. The field covers everything from information extraction and translation to discourse, pragmatics, evaluation, and the social contexts in which language technologies operate.
What it is
NLP develops systems that analyze or produce language in text and speech. It must contend with ambiguity, context, variation across communities, changing usage, and the fact that meaning often depends on the world beyond the words.
How it works
Systems may tokenize and represent language, identify structure, retrieve evidence, infer labels, or generate responses. Modern pipelines often combine pretrained models with task data, retrieval, tools, and human review, with evaluation designed for the intended users and domain.
Key concepts
- Syntax and semantics
- Discourse and pragmatics
- Language representation
- Information extraction
- Machine translation
- Grounding and evaluation
Current research questions
- How can systems represent meaning across languages, dialects, and domains?
- How should open-ended generation be evaluated for usefulness and truthfulness?
- Can language models ground claims in verifiable context?
- How can communities participate in defining language technology quality?
Applications
- Search and question answering
- Translation and accessibility
- Document analysis
- Speech interfaces
- Public-health and scientific text mining
Relevant research papers
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