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127 lines
4.4 KiB
Plaintext
127 lines
4.4 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY 101 > ARCHITECTURE
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p
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| The central data structures in spaCy are the #[code Doc] and the
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| #[code Vocab]. The #[code Doc] object owns the
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| #[strong sequence of tokens] and all their annotations. The #[code Vocab]
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| object owns a set of #[strong look-up tables] that make common
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| information available across documents. By centralising strings, word
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| vectors and lexical attributes, we avoid storing multiple copies of this
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| data. This saves memory, and ensures there's a
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| #[strong single source of truth].
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p
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| Text annotations are also designed to allow a single source of truth: the
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| #[code Doc] object owns the data, and #[code Span] and #[code Token] are
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| #[strong views that point into it]. The #[code Doc] object is constructed
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| by the #[code Tokenizer], and then #[strong modified in place] by the
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| components of the pipeline. The #[code Language] object coordinates these
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| components. It takes raw text and sends it through the pipeline,
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| returning an #[strong annotated document]. It also orchestrates training
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| and serialization.
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+image
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include ../../../assets/img/docs/architecture.svg
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.u-text-right
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+button("/assets/img/docs/architecture.svg", false, "secondary").u-text-tag View large graphic
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+table(["Name", "Description"])
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+row
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+cell #[+api("language") #[code Language]]
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+cell
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| A text-processing pipeline. Usually you'll load this once per
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| process as #[code nlp] and pass the instance around your application.
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+row
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+cell #[+api("doc") #[code Doc]]
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+cell A container for accessing linguistic annotations.
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+row
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+cell #[+api("span") #[code Span]]
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+cell A slice from a #[code Doc] object.
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+row
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+cell #[+api("token") #[code Token]]
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+cell
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| An individual token — i.e. a word, punctuation symbol, whitespace,
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| etc.
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+row
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+cell #[+api("lexeme") #[code Lexeme]]
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+cell
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| An entry in the vocabulary. It's a word type with no context, as
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| opposed to a word token. It therefore has no part-of-speech tag,
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| dependency parse etc.
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+row
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+cell #[+api("vocab") #[code Vocab]]
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+cell
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| A lookup table for the vocabulary that allows you to access
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| #[code Lexeme] objects.
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+row
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+cell #[code Morphology]
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+cell
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| Assign linguistic features like lemmas, noun case, verb tense etc.
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| based on the word and its part-of-speech tag.
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+row
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+cell #[+api("stringstore") #[code StringStore]]
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+cell Map strings to and from hash values.
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+row
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+cell #[+api("tokenizer") #[code Tokenizer]]
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+cell
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| Segment text, and create #[code Doc] objects with the discovered
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| segment boundaries.
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+row
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+cell #[code Lemmatizer]
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+cell
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| Determine the base forms of words.
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+row
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+cell #[+api("matcher") #[code Matcher]]
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+cell
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| Match sequences of tokens, based on pattern rules, similar to
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| regular expressions.
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+h(3, "architecture-pipeline") Pipeline components
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+table(["Name", "Description"])
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+row
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+cell #[+api("tagger") #[code Tagger]]
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+cell Annotate part-of-speech tags on #[code Doc] objects.
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+row
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+cell #[+api("dependencyparser") #[code DependencyParser]]
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+cell Annotate syntactic dependencies on #[code Doc] objects.
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+row
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+cell #[+api("entityrecognizer") #[code EntityRecognizer]]
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+cell
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| Annotate named entities, e.g. persons or products, on #[code Doc]
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| objects.
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+h(3, "architecture-other") Other classes
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+table(["Name", "Description"])
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+row
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+cell #[+api("vectors") #[code Vectors]]
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+cell Container class for vector data keyed by string.
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+row
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+cell #[+api("binder") #[code Binder]]
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+cell Container class for serializing collections of #[code Doc] objects.
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+row
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+cell #[+api("goldparse") #[code GoldParse]]
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+cell Collection for training annotations.
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+row
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+cell #[+api("goldcorpus") #[code GoldCorpus]]
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+cell
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| An annotated corpus, using the JSON file format. Manages
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| annotations for tagging, dependency parsing and NER.
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