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141 lines
4.8 KiB
Plaintext
141 lines
4.8 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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+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.en import English
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nlp = English(parser=False)
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tokens = nlp('Some\nspaces and\ttab characters')
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print([t.orth_ for t in tokens])
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# ['Some', '\n', 'spaces', ' ', 'and', '\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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+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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+h(2, "pos-tagging") Part-of-speech Tagging
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include _annotation/_pos-tags
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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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+aside("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.u-nowrap -PRON-], which is used as the lemma for
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| all personal pronouns.
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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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+h(2, "dependency-parsing") Syntactic Dependency Parsing
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include _annotation/_dep-labels
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+h(2, "named-entities") Named Entity Recognition
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include _annotation/_named-entities
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+h(3, "biluo") BILUO Scheme
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p
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| spaCy translates character offsets into the BILUO scheme, in order to
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| decide the cost of each action given the current state of the entity
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| recognizer. The costs are then used to calculate the gradient of the
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| loss, to train the model.
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+aside("Why BILUO, not IOB?")
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| There are several coding schemes for encoding entity annotations as
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| token tags. These coding schemes are equally expressive, but not
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| necessarily equally learnable.
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| #[+a("http://www.aclweb.org/anthology/W09-1119") Ratinov and Roth]
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| showed that the minimal #[strong Begin], #[strong In], #[strong Out]
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| scheme was more difficult to learn than the #[strong BILUO] scheme that
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| we use, which explicitly marks boundary tokens.
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+table([ "Tag", "Description" ])
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+row
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+cell #[code #[span.u-color-theme B] EGIN]
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+cell The first token of a multi-token entity.
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+row
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+cell #[code #[span.u-color-theme I] N]
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+cell An inner token of a multi-token entity.
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+row
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+cell #[code #[span.u-color-theme L] AST]
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+cell The final token of a multi-token entity.
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+row
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+cell #[code #[span.u-color-theme U] NIT]
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+cell A single-token entity.
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+row
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+cell #[code #[span.u-color-theme O] UT]
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+cell A non-entity token.
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+h(2, "json-input") JSON input format for training
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p
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| spaCy takes training data in the following format:
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+code("Example structure").
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doc: {
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id: string,
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paragraphs: [{
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raw: string,
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sents: [int],
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tokens: [{
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start: int,
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tag: string,
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head: int,
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dep: string
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}],
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ner: [{
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start: int,
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end: int,
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label: string
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}],
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brackets: [{
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start: int,
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end: int,
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label: string
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}]
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}]
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}
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