spaCy/website/docs/usage/101/_architecture.md

The central data structures in spaCy are the Doc and the Vocab. The Doc object owns the sequence of tokens and all their annotations. The Vocab object owns a set of look-up tables that make common information available across documents. By centralizing strings, word vectors and lexical attributes, we avoid storing multiple copies of this data. This saves memory, and ensures there's a single source of truth.

Text annotations are also designed to allow a single source of truth: the Doc object owns the data, and Span and Token are views that point into it. The Doc object is constructed by the Tokenizer, and then modified in place by the components of the pipeline. The Language object coordinates these components. It takes raw text and sends it through the pipeline, returning an annotated document. It also orchestrates training and serialization.

Container objects

Name	Description
Doc	A container for accessing linguistic annotations.
Span	A slice from a Doc object.
Token	An individual token — i.e. a word, punctuation symbol, whitespace, etc.
Lexeme	An entry in the vocabulary. It's a word type with no context, as opposed to a word token. It therefore has no part-of-speech tag, dependency parse etc.

Processing pipeline

Name	Description
Language	A text-processing pipeline. Usually you'll load this once per process as nlp and pass the instance around your application.
Tokenizer	Segment text, and create Doc objects with the discovered segment boundaries.
Lemmatizer	Determine the base forms of words.
Morphology	Assign linguistic features like lemmas, noun case, verb tense etc. based on the word and its part-of-speech tag.
Tagger	Annotate part-of-speech tags on Doc objects.
DependencyParser	Annotate syntactic dependencies on Doc objects.
EntityRecognizer	Annotate named entities, e.g. persons or products, on Doc objects.
TextCategorizer	Assign categories or labels to Doc objects.
Matcher	Match sequences of tokens, based on pattern rules, similar to regular expressions.
PhraseMatcher	Match sequences of tokens based on phrases.
EntityRuler	Add entity spans to the Doc using token-based rules or exact phrase matches.
SentenceSegmenter	Implement custom sentence boundary detection logic that doesn't require the dependency parse.
Other functions	Automatically apply something to the Doc, e.g. to merge spans of tokens.

Other classes

Name	Description
Vocab	A lookup table for the vocabulary that allows you to access Lexeme objects.
StringStore	Map strings to and from hash values.
Vectors	Container class for vector data keyed by string.
GoldParse	Collection for training annotations.
GoldCorpus	An annotated corpus, using the JSON file format. Manages annotations for tagging, dependency parsing and NER.