mirror of https://github.com/explosion/spaCy.git
196 lines
5.5 KiB
Plaintext
196 lines
5.5 KiB
Plaintext
//- 💫 DOCS > USAGE > SPACY 101
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include ../../_includes/_mixins
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+h(2, "features") Features
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+aside
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| If one of spaCy's functionalities #[strong needs a model], it means that
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| you need to have one our the available
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| #[+a("/docs/usage/models") statistical models] installed. Models are used
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| to #[strong predict] linguistic annotations – for example, if a word is
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| a verb or a noun.
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+table(["Name", "Description", "Needs model"])
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+row
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+cell #[strong Tokenization]
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+cell
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+cell #[+procon("con")]
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+row
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+cell #[strong Part-of-speech Tagging]
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+cell
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+cell #[+procon("pro")]
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+row
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+cell #[strong Dependency Parsing]
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+cell
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+cell #[+procon("pro")]
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+row
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+cell #[strong Sentence Boundary Detection]
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+cell
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+cell #[+procon("pro")]
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+row
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+cell #[strong Named Entity Recongition] (NER)
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+cell
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+cell #[+procon("pro")]
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+row
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+cell #[strong Rule-based Matching]
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+cell
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+cell #[+procon("con")]
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+row
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+cell #[strong Similarity]
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+cell
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+cell #[+procon("pro")]
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+row
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+cell #[strong Training]
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+cell
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+cell #[+procon("neutral")]
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+row
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+cell #[strong Serialization]
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+cell
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+cell #[+procon("neutral")]
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+h(2, "annotations") Linguistic annotations
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p
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| spaCy provides a variety of linguistic annotations to give you insights
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| into a text's grammatical structure. This includes the word types,
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| i.e. the parts of speech, and how the words are related to each other.
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| For example, if you're analysing text, it makes a #[em huge] difference
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| whether a noun is the subject of a sentence, or the object – or whether
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| "google" is used as a verb, or refers to the website or company in a
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| specific context.
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p
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| Once you've downloaded and installed a #[+a("/docs/usage/models") model],
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| you can load it via #[+api("spacy#load") #[code spacy.load()]]. This will
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| return a #[code Language] object contaning all components and data needed
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| to process text. We usually call it #[code nlp]. Calling the #[code nlp]
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| object on a string of text will return a processed #[code Doc]:
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+code.
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import spacy
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nlp = spacy.load('en')
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doc = nlp(u'Apple is looking at buying U.K. startup for $1 billion')
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+h(3, "annotations-token") Tokenization
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include _spacy-101/_tokenization
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+h(3, "annotations-pos-deps") Part-of-speech tags and dependencies
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+tag-model("dependency parse")
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include _spacy-101/_pos-deps
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+h(3, "annotations-ner") Named Entities
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+tag-model("named entities")
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include _spacy-101/_named-entities
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+h(2, "vectors-similarity") Word vectors and similarity
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+tag-model("vectors")
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include _spacy-101/_similarity
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include _spacy-101/_word-vectors
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+h(2, "pipelines") Pipelines
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+h(2, "architecture") Architecture
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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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+row
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+cell #[+api("stringstore") #[code StringStore]]
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+cell Map strings to and from integer IDs.
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+row
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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 #[+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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+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-other") Other
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+table(["Name", "Description"])
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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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