mirror of https://github.com/explosion/spaCy.git
53 lines
5.8 KiB
Markdown
53 lines
5.8 KiB
Markdown
The central data structures in spaCy are the `Doc` and the `Vocab`. The `Doc`
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object owns the **sequence of tokens** and all their annotations. The `Vocab`
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object owns a set of **look-up tables** that make common information available
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across documents. By centralizing strings, word vectors and lexical attributes,
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we avoid storing multiple copies of this data. This saves memory, and ensures
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there's a **single source of truth**.
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Text annotations are also designed to allow a single source of truth: the `Doc`
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object owns the data, and `Span` and `Token` are **views that point into it**.
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The `Doc` object is constructed by the `Tokenizer`, and then **modified in
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place** by the components of the pipeline. The `Language` object coordinates
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these components. It takes raw text and sends it through the pipeline, returning
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an **annotated document**. It also orchestrates training and serialization.
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### Container objects {#architecture-containers}
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| Name | Description |
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| ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`Doc`](/api/doc) | A container for accessing linguistic annotations. |
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| [`Span`](/api/span) | A slice from a `Doc` object. |
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| [`Token`](/api/token) | An individual token — i.e. a word, punctuation symbol, whitespace, etc. |
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| [`Lexeme`](/api/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. |
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### Processing pipeline {#architecture-pipeline}
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| Name | Description |
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| --------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- |
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| [`Language`](/api/language) | A text-processing pipeline. Usually you'll load this once per process as `nlp` and pass the instance around your application. |
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| [`Tokenizer`](/api/tokenizer) | Segment text, and create `Doc` objects with the discovered segment boundaries. |
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| [`Lemmatizer`](/api/lemmatizer) | Determine the base forms of words. |
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| `Morphology` | Assign linguistic features like lemmas, noun case, verb tense etc. based on the word and its part-of-speech tag. |
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| [`Tagger`](/api/tagger) | Annotate part-of-speech tags on `Doc` objects. |
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| [`DependencyParser`](/api/dependencyparser) | Annotate syntactic dependencies on `Doc` objects. |
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| [`EntityRecognizer`](/api/entityrecognizer) | Annotate named entities, e.g. persons or products, on `Doc` objects. |
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| [`TextCategorizer`](/api/textcategorizer) | Assign categories or labels to `Doc` objects. |
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| [`Matcher`](/api/matcher) | Match sequences of tokens, based on pattern rules, similar to regular expressions. |
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| [`PhraseMatcher`](/api/phrasematcher) | Match sequences of tokens based on phrases. |
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| [`EntityRuler`](/api/entityruler) | Add entity spans to the `Doc` using token-based rules or exact phrase matches. |
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| [`SentenceSegmenter`](/api/sentencesegmenter) | Implement custom sentence boundary detection logic that doesn't require the dependency parse. |
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| [Other functions](/api/pipeline-functions) | Automatically apply something to the `Doc`, e.g. to merge spans of tokens. |
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### Other classes {#architecture-other}
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| Name | Description |
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| --------------------------------- | ------------------------------------------------------------------------------------------------------------- |
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| [`Vocab`](/api/vocab) | A lookup table for the vocabulary that allows you to access `Lexeme` objects. |
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| [`StringStore`](/api/stringstore) | Map strings to and from hash values. |
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| [`Vectors`](/api/vectors) | Container class for vector data keyed by string. |
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| [`GoldParse`](/api/goldparse) | Collection for training annotations. |
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| [`GoldCorpus`](/api/goldcorpus) | An annotated corpus, using the JSON file format. Manages annotations for tagging, dependency parsing and NER. |
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