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105 lines
4.1 KiB
Plaintext
105 lines
4.1 KiB
Plaintext
//- 💫 DOCS > API > ANNOTATION SPECS
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include ../_includes/_mixins
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p This document describes the target annotations spaCy is trained to predict.
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+section("tokenization")
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+h(2, "tokenization") Tokenization
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p
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| Tokenization standards are based on the
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| #[+a("https://catalog.ldc.upenn.edu/LDC2013T19") OntoNotes 5] corpus.
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| The tokenizer differs from most by including tokens for significant
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| whitespace. Any sequence of whitespace characters beyond a single space
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| (#[code ' ']) is included as a token.
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+aside-code("Example").
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from spacy.lang.en import English
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nlp = English()
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tokens = nlp('Some\nspaces and\ttab characters')
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tokens_text = [t.text for t in tokens]
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assert tokens_text == ['Some', '\n', 'spaces', ' ', 'and',
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'\t', 'tab', 'characters']
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p
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| The whitespace tokens are useful for much the same reason punctuation is
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| – it's often an important delimiter in the text. By preserving it in the
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| token output, we are able to maintain a simple alignment between the
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| tokens and the original string, and we ensure that no information is
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| lost during processing.
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+section("sbd")
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+h(2, "sentence-boundary") Sentence boundary detection
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p
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| Sentence boundaries are calculated from the syntactic parse tree, so
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| features such as punctuation and capitalisation play an important but
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| non-decisive role in determining the sentence boundaries. Usually this
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| means that the sentence boundaries will at least coincide with clause
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| boundaries, even given poorly punctuated text.
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+section("pos-tagging")
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+h(2, "pos-tagging") Part-of-speech Tagging
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+aside("Tip: Understanding tags")
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| You can also use #[code spacy.explain()] to get the description for the
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| string representation of a tag. For example,
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| #[code spacy.explain("RB")] will return "adverb".
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include _annotation/_pos-tags
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+section("lemmatization")
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+h(2, "lemmatization") Lemmatization
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p A "lemma" is the uninflected form of a word. In English, this means:
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+list
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+item #[strong Adjectives]: The form like "happy", not "happier" or "happiest"
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+item #[strong Adverbs]: The form like "badly", not "worse" or "worst"
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+item #[strong Nouns]: The form like "dog", not "dogs"; like "child", not "children"
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+item #[strong Verbs]: The form like "write", not "writes", "writing", "wrote" or "written"
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p
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| The lemmatization data is taken from
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| #[+a("https://wordnet.princeton.edu") WordNet]. However, we also add a
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| special case for pronouns: all pronouns are lemmatized to the special
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| token #[code -PRON-].
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+infobox("About spaCy's custom pronoun lemma")
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| Unlike verbs and common nouns, there's no clear base form of a personal
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| pronoun. Should the lemma of "me" be "I", or should we normalize person
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| as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a
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| novel symbol, #[code -PRON-], which is used as the lemma for
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| all personal pronouns.
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+section("dependency-parsing")
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+h(2, "dependency-parsing") Syntactic Dependency Parsing
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+aside("Tip: Understanding labels")
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| You can also use #[code spacy.explain()] to get the description for the
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| string representation of a label. For example,
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| #[code spacy.explain("prt")] will return "particle".
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include _annotation/_dep-labels
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+section("named-entities")
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+h(2, "named-entities") Named Entity Recognition
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+aside("Tip: Understanding entity types")
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| You can also use #[code spacy.explain()] to get the description for the
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| string representation of an entity label. For example,
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| #[code spacy.explain("LANGUAGE")] will return "any named language".
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include _annotation/_named-entities
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+h(3, "biluo") BILUO Scheme
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include _annotation/_biluo
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+section("training")
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+h(2, "json-input") JSON input format for training
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include _annotation/_training
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