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* Draft out initial Spans data structure * Initial span group commit * Basic span group support on Doc * Basic test for span group * Compile span_group.pyx * Draft addition of SpanGroup to DocBin * Add deserialization for SpanGroup * Add tests for serializing SpanGroup * Fix serialization of SpanGroup * Add EdgeC and GraphC structs * Add draft Graph data structure * Compile graph * More work on Graph * Update GraphC * Upd graph * Fix walk functions * Let Graph take nodes and edges on construction * Fix walking and getting * Add graph tests * Fix import * Add module with the SpanGroups dict thingy * Update test * Rename 'span_groups' attribute * Try to fix c++11 compilation * Fix test * Update DocBin * Try to fix compilation * Try to fix graph * Improve SpanGroup docstrings * Add doc.spans to documentation * Fix serialization * Tidy up and add docs * Update docs [ci skip] * Add SpanGroup.has_overlap * WIP updated Graph API * Start testing new Graph API * Update Graph tests * Update Graph * Add docstring Co-authored-by: Ines Montani <ines@ines.io>
88 lines
9.2 KiB
Markdown
88 lines
9.2 KiB
Markdown
The central data structures in spaCy are the [`Language`](/api/language) class,
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the [`Vocab`](/api/vocab) and the [`Doc`](/api/doc) object. The `Language` class
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is used to process a text and turn it into a `Doc` object. It's typically stored
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as a variable called `nlp`. The `Doc` object owns the **sequence of tokens** and
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all their annotations. By centralizing strings, word vectors and lexical
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attributes in the `Vocab`, we avoid storing multiple copies of this data. This
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saves memory, and ensures 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`](/api/span) and [`Token`](/api/token) are
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**views that point into it**. The `Doc` object is constructed by the
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[`Tokenizer`](/api/tokenizer), and then **modified in place** by the components
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of the pipeline. The `Language` object coordinates these components. It takes
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raw text and sends it through the pipeline, returning an **annotated document**.
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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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| [`DocBin`](/api/docbin) | A collection of `Doc` objects for efficient binary serialization. Also used for [training data](/api/data-formats#binary-training). |
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| [`Example`](/api/example) | A collection of training annotations, containing two `Doc` objects: the reference data and the predictions. |
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| [`Language`](/api/language) | Processing class that turns text into `Doc` objects. Different languages implement their own subclasses of it. The variable is typically called `nlp`. |
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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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| [`Span`](/api/span) | A slice from a `Doc` object. |
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| [`SpanGroup`](/api/spangroup) | A named collection of spans belonging to a `Doc`. |
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| [`Token`](/api/token) | An individual token — i.e. a word, punctuation symbol, whitespace, etc. |
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### Processing pipeline {#architecture-pipeline}
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The processing pipeline consists of one or more **pipeline components** that are
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called on the `Doc` in order. The tokenizer runs before the components. Pipeline
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components can be added using [`Language.add_pipe`](/api/language#add_pipe).
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They can contain a statistical model and trained weights, or only make
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rule-based modifications to the `Doc`. spaCy provides a range of built-in
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components for different language processing tasks and also allows adding
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[custom components](/usage/processing-pipelines#custom-components).
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| Name | Description |
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| ----------------------------------------------- | ------------------------------------------------------------------------------------------- |
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| [`AttributeRuler`](/api/attributeruler) | Set token attributes using matcher rules. |
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| [`DependencyParser`](/api/dependencyparser) | Predict syntactic dependencies. |
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| [`EntityLinker`](/api/entitylinker) | Disambiguate named entities to nodes in a knowledge base. |
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| [`EntityRecognizer`](/api/entityrecognizer) | Predict named entities, e.g. persons or products. |
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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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| [`Lemmatizer`](/api/lemmatizer) | Determine the base forms of words. |
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| [`Morphologizer`](/api/morphologizer) | Predict morphological features and coarse-grained part-of-speech tags. |
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| [`SentenceRecognizer`](/api/sentencerecognizer) | Predict sentence boundaries. |
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| [`Sentencizer`](/api/sentencizer) | Implement rule-based sentence boundary detection that doesn't require the dependency parse. |
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| [`Tagger`](/api/tagger) | Predict part-of-speech tags. |
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| [`TextCategorizer`](/api/textcategorizer) | Predict categories or labels over the whole document. |
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| [`Tok2Vec`](/api/tok2vec) | Apply a "token-to-vector" model and set its outputs. |
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| [`Tokenizer`](/api/tokenizer) | Segment raw text and create `Doc` objects from the words. |
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| [`TrainablePipe`](/api/pipe) | Class that all trainable pipeline components inherit from. |
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| [`Transformer`](/api/transformer) | Use a transformer model and set its outputs. |
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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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### Matchers {#architecture-matchers}
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Matchers help you find and extract information from [`Doc`](/api/doc) objects
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based on match patterns describing the sequences you're looking for. A matcher
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operates on a `Doc` and gives you access to the matched tokens **in context**.
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| Name | Description |
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| --------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| [`DependencyMatcher`](/api/dependencymatcher) | Match sequences of tokens based on dependency trees using [Semgrex operators](https://nlp.stanford.edu/nlp/javadoc/javanlp/edu/stanford/nlp/semgraph/semgrex/SemgrexPattern.html). |
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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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### Other classes {#architecture-other}
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| Name | Description |
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| ------------------------------------------------ | -------------------------------------------------------------------------------------------------- |
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| [`Corpus`](/api/corpus) | Class for managing annotated corpora for training and evaluation data. |
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| [`KnowledgeBase`](/api/kb) | Storage for entities and aliases of a knowledge base for entity linking. |
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| [`Lookups`](/api/lookups) | Container for convenient access to large lookup tables and dictionaries. |
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| [`MorphAnalysis`](/api/morphology#morphanalysis) | A morphological analysis. |
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| [`Morphology`](/api/morphology) | Store morphological analyses and map them to and from hash values. |
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| [`Scorer`](/api/scorer) | Compute evaluation scores. |
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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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| [`Vocab`](/api/vocab) | The shared vocabulary that stores strings and gives you access to [`Lexeme`](/api/lexeme) objects. |
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