mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-01 00:17:44 +03:00
127 lines
4.4 KiB
Plaintext
127 lines
4.4 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY 101 > ARCHITECTURE
|
|
|
|
p
|
|
| The central data structures in spaCy are the #[code Doc] and the
|
|
| #[code Vocab]. The #[code Doc] object owns the
|
|
| #[strong sequence of tokens] and all their annotations. The #[code Vocab]
|
|
| object owns a set of #[strong look-up tables] that make common
|
|
| information available across documents. By centralising strings, word
|
|
| vectors and lexical attributes, we avoid storing multiple copies of this
|
|
| data. This saves memory, and ensures there's a
|
|
| #[strong single source of truth].
|
|
|
|
p
|
|
| Text annotations are also designed to allow a single source of truth: the
|
|
| #[code Doc] object owns the data, and #[code Span] and #[code Token] are
|
|
| #[strong views that point into it]. The #[code Doc] object is constructed
|
|
| by the #[code Tokenizer], and then #[strong modified in place] by the
|
|
| components of the pipeline. The #[code Language] object coordinates these
|
|
| components. It takes raw text and sends it through the pipeline,
|
|
| returning an #[strong annotated document]. It also orchestrates training
|
|
| and serialization.
|
|
|
|
+image
|
|
include ../../../assets/img/docs/architecture.svg
|
|
.u-text-right
|
|
+button("/assets/img/docs/architecture.svg", false, "secondary").u-text-tag View large graphic
|
|
|
|
+table(["Name", "Description"])
|
|
+row
|
|
+cell #[+api("language") #[code Language]]
|
|
+cell
|
|
| A text-processing pipeline. Usually you'll load this once per
|
|
| process as #[code nlp] and pass the instance around your application.
|
|
|
|
+row
|
|
+cell #[+api("doc") #[code Doc]]
|
|
+cell A container for accessing linguistic annotations.
|
|
|
|
+row
|
|
+cell #[+api("span") #[code Span]]
|
|
+cell A slice from a #[code Doc] object.
|
|
|
|
+row
|
|
+cell #[+api("token") #[code Token]]
|
|
+cell
|
|
| An individual token — i.e. a word, punctuation symbol, whitespace,
|
|
| etc.
|
|
|
|
+row
|
|
+cell #[+api("lexeme") #[code Lexeme]]
|
|
+cell
|
|
| 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.
|
|
|
|
+row
|
|
+cell #[+api("vocab") #[code Vocab]]
|
|
+cell
|
|
| A lookup table for the vocabulary that allows you to access
|
|
| #[code Lexeme] objects.
|
|
|
|
+row
|
|
+cell #[code Morphology]
|
|
+cell
|
|
| Assign linguistic features like lemmas, noun case, verb tense etc.
|
|
| based on the word and its part-of-speech tag.
|
|
|
|
+row
|
|
+cell #[+api("stringstore") #[code StringStore]]
|
|
+cell Map strings to and from hash values.
|
|
|
|
+row
|
|
+cell #[+api("tokenizer") #[code Tokenizer]]
|
|
+cell
|
|
| Segment text, and create #[code Doc] objects with the discovered
|
|
| segment boundaries.
|
|
|
|
+row
|
|
+cell #[code Lemmatizer]
|
|
+cell
|
|
| Determine the base forms of words.
|
|
|
|
+row
|
|
+cell #[+api("matcher") #[code Matcher]]
|
|
+cell
|
|
| Match sequences of tokens, based on pattern rules, similar to
|
|
| regular expressions.
|
|
|
|
|
|
+h(3, "architecture-pipeline") Pipeline components
|
|
|
|
+table(["Name", "Description"])
|
|
+row
|
|
+cell #[+api("tagger") #[code Tagger]]
|
|
+cell Annotate part-of-speech tags on #[code Doc] objects.
|
|
|
|
+row
|
|
+cell #[+api("dependencyparser") #[code DependencyParser]]
|
|
+cell Annotate syntactic dependencies on #[code Doc] objects.
|
|
|
|
+row
|
|
+cell #[+api("entityrecognizer") #[code EntityRecognizer]]
|
|
+cell
|
|
| Annotate named entities, e.g. persons or products, on #[code Doc]
|
|
| objects.
|
|
|
|
+h(3, "architecture-other") Other classes
|
|
|
|
+table(["Name", "Description"])
|
|
+row
|
|
+cell #[+api("vectors") #[code Vectors]]
|
|
+cell Container class for vector data keyed by string.
|
|
|
|
+row
|
|
+cell #[+api("binder") #[code Binder]]
|
|
+cell Container class for serializing collections of #[code Doc] objects.
|
|
|
|
+row
|
|
+cell #[+api("goldparse") #[code GoldParse]]
|
|
+cell Collection for training annotations.
|
|
|
|
+row
|
|
+cell #[+api("goldcorpus") #[code GoldCorpus]]
|
|
+cell
|
|
| An annotated corpus, using the JSON file format. Manages
|
|
| annotations for tagging, dependency parsing and NER.
|