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NLP stands for natural language processing: the area of computing and artificial intelligence concerned with working with human language in text and speech. It brings together computational linguistics, statistics, machine learning, and deep learning. NLP is not one model or method—it is a collection of tasks such as translating text, identifying names, analyzing sentiment, and recognizing speech. This representative glossary explains core terms and how they fit together; it is not a complete inventory for every letter of the alphabet.
How NLP systems work
A useful simplified picture is: prepare language data, divide it into units, represent those units numerically, analyze them for a particular task, and produce an output. The actual steps vary. Some systems use hand-written linguistic rules; others learn patterns from examples, and modern neural models may handle text in ways that do not require the same explicit preprocessing pipeline. IBM’s overview describes common workflow stages including preprocessing, feature extraction, text analysis, and model training (IBM’s NLP overview).
- Prepare the input. Depending on the task and model, text may be normalized or otherwise prepared. These operations are not mandatory in a fixed sequence for every NLP system.
- Divide it into units. Tokenization splits text into units called tokens, which may be words, subwords, or other model-specific pieces.
- Represent the units numerically. A system may use counts or TF-IDF features, or learned vector representations such as embeddings.
- Analyze or generate language. A model may classify text, extract information, translate it, or generate a response, among other tasks.
NLP appears in applications such as search, spell checking, text prediction, chatbots, voice assistants, summarization, and speech-to-text. These are examples of uses, not evidence that all products use the same techniques (Stanford HAI; NNLM).
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NLP terms from A to Z
A — Ambiguity
Ambiguity occurs when a word or sentence allows more than one interpretation. For example, “bank” might refer to a financial institution or the side of a river. Context helps a system choose a likely meaning, but the intended meaning may still be unclear.
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C — Computational linguistics and coreference resolution
Computational linguistics applies computational methods to language. Its contributions include linguistic analysis and rule-based approaches, alongside statistical and machine-learning methods.
Coreference resolution identifies expressions that refer to the same entity. In “Maya opened the window because she was hot,” a system may determine that “she” refers to Maya.
D — Deep learning
Deep learning uses neural networks with multiple processing layers to learn patterns from data. It is widely used for NLP, but it is one approach within the field—not the only valid way to process language.
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E — Embeddings and feature representations
Computers need numerical representations to process text. A traditional Bag of Words representation records which words appear or how often, often without preserving word order. TF-IDF weights terms according to their frequency in a document and how common they are across a collection. These features can be useful for tasks such as document classification or search.
An embedding represents a word or other unit as a vector of numbers. Dense embeddings can encode patterns among units, while contextual representations can vary according to the surrounding text. The appropriate representation depends on the task and data; a newer or denser representation is not automatically better for every use.
G — GPT and grammar tagging
GPT is a family of machine-learning models based on the transformer architecture. NIST defines GPT as models pretrained through self-supervised learning on large datasets of unlabelled text, and describes transformers as the predominant architecture for large language models in its NIST AI 100-2e2025 glossary. GPT is not another name for NLP: NLP is the wider field, while GPT refers to a particular family of transformer-based models.
Part-of-speech (POS) tagging assigns grammatical categories—such as noun, verb, or adjective—to words in context. A word’s role may depend on how it is used in a sentence.
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A language model models patterns in language. Models differ in their architecture, training, and purpose. GPT is one family of language models; the term “language model” is broader and does not by itself specify that a system is a transformer or a text generator.
M — Machine learning and machine translation
Machine learning methods infer patterns from data rather than relying exclusively on rules written by people. Their behavior depends on the examples and evaluation conditions used to develop them.
Machine translation uses computing methods to translate text or speech between languages. It is a longstanding NLP task, though the techniques used can vary.
N — Named entity recognition and natural language understanding
Named entity recognition (NER) finds mentions of entities in text and labels categories such as people, organizations, and locations. For example, an NER system might identify a person’s name and the city mentioned in a sentence.
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P — Parsing and preprocessing
Parsing analyzes the grammatical structure of a sentence. Dependency parsing, for example, represents relationships between words, such as which word acts as the subject of a verb.
Preprocessing refers to preparing text for a system. It may include tokenization or normalization, but the appropriate steps depend on the task and model. Removing punctuation or changing letter case, for instance, may help one task and harm another.
S — Sentiment analysis, self-attention, and speech recognition
Sentiment analysis classifies text according to expressed sentiment or emotional polarity, such as positive, negative, or neutral. It can miss context, mixed opinions, or irony.
Self-attention is a mechanism used by transformers to model relationships between positions in a sequence. It helps a model use surrounding tokens when processing a particular token.
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Speech recognition converts spoken audio into text. Speech systems can be affected by pronunciation, dialect, speaking conditions, and background noise.
T — Tokenization and transformers
Tokenization divides input into tokens for processing. Tokens are not always whole words: some systems use subword pieces, so the same word may be represented by one or several tokens.
A transformer is a neural-network architecture that uses self-attention to model relationships among tokens. Transformers underpin many current language models, including GPT, but the architecture does not define the whole field of NLP.
W — Word-sense disambiguation
Word-sense disambiguation is the task of selecting which meaning of a word is intended in context. It is closely related to ambiguity: “bank” in a sentence about deposits likely has a different sense from “bank” in a sentence about a river.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How the main approaches differ
NLP methods are better understood as choices for particular tasks than as a simple progression in which one generation makes all earlier methods obsolete.
| Approach | How it works | Useful distinction |
|---|---|---|
| Rules-based systems | Apply linguistic or other rules written by people. | Rules can be understandable and effective for a narrow, well-defined problem. IBM’s broad overview notes limits to scaling rule-based systems; that should not be taken as a claim that every rules-based system is unsuitable. |
| Statistical and machine-learning systems | Infer patterns from data, sometimes using features such as word counts or TF-IDF. | Results depend on the data, task, and evaluation conditions; learned behavior is not automatically reliable in a different setting. |
| Deep-learning and transformer systems | Learn neural representations; transformers use self-attention to model relationships between tokens. | They support a range of tasks, including classification, extraction, translation, and generation. They are methods within NLP, not synonyms for NLP. |
What NLP can do—and where it can fail
NLP systems can support useful language tasks, but language is variable and context-dependent. A system that works well on one dataset or population may not perform as well on another. There is no single accuracy figure that describes NLP as a whole.
- Meaning depends on context. Ambiguous words, idioms, and references to earlier parts of a conversation can be difficult to interpret.
- Language varies. Slang, dialect, changing vocabulary, and grammatical variation can differ from patterns represented in a system’s data.
- Tone is hard to infer. Sarcasm, irony, and mixed sentiment can be misread, especially when a system sees text without conversational or situational context.
- Speech has additional challenges. Pronunciation, accents, and background noise can affect speech recognition.
- Data can carry bias. Patterns in training data can skew system outputs, so performance and consequences should be assessed for the language, population, and task at hand.
Where to learn more
For a course-length reference, Stanford hosts the third edition of Speech and Language Processing, which covers foundational algorithms, transformers, speech, sequence labeling, and coreference resolution: Speech and Language Processing. The site’s available format or contents may change over time.
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