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119 lines
4.2 KiB
Plaintext
119 lines
4.2 KiB
Plaintext
include ../../_includes/_mixins
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+lead spaCy assumes by default that your data is raw text. However, sometimes your data is partially annotated, e.g. with pre-existing tokenization, part-of-speech tags, etc. This tutorial explains how to use these annotations in spaCy.
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+h2 Quick Reference
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+table(['Description', 'Usage'], 'code')
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+row
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+cell Use pre-existing tokenization
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+cell #[code.lang-python doc = Doc(nlp.vocab, [('A', True), ('token', False), ('!', False)])]
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+row
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+cell Use pre-existing tokenization (deprecated)
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+cell #[code.lang-python doc = nlp.tokenizer.tokens_from_list([u'A', u'token', u'!'])]
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+row
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+cell Assign pre-existing tags
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+cell #[code.lang-python nlp.tagger.tag_from_strings(doc, ['DT', 'NN'])]
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+row
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+cell Assign named entity annotations from an array
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+cell #[code.lang-python doc.from_array([ENT_TYPE, ENT_IOB], values)]
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+row
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+cell Assign dependency parse annotations from an array
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+cell #[code.lang-python doc.from_array([HEAD, DEP], values)]
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+h2 Examples
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+code('python', 'Tokenization').
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import spacy
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nlp = spacy.load('en')
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tokens = [u'A', u'list', u'of', u'strings', u'.']
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doc = nlp.tokenizer.tokens_from_list(tokens)
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assert len(doc) == len(tokens)
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# With this method, we don't get to specify how the corresponding string
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# would be spaced, so we have to assume a space before every token.
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assert doc.text == u'A list of strings .'
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+code('python', 'Tokenization').
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import spacy
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from spacy.tokens import Doc
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nlp = spacy.load('en')
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tokens = [u'A', u'list', u'of', u'strings', u'.']
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has_space = [True, True, True, False, False]
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doc = Doc(nlp.vocab, orth_and_spaces=zip(tokens, has_space))
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assert len(doc) == len(tokens)
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# Spacing is correct, given by boolean values above.
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assert doc.text == u'A list of strings.'
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# Here's how it would look with different boolean values.
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tokens = [u'A', u'list', u'of', u'strings', u'.']
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has_space = [False, True, True, True, False]
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doc = Doc(nlp.vocab, orth_and_spaces=zip(tokens, has_space))
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assert doc.text == u'Alist of strings .'
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+code('python', 'POS Tags').
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import spacy
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nlp = spacy.load('en')
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# Tokenize a string into a Doc, but don't apply the whole pipeline ---
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# that is, don't predict the part-of-speech tags, syntactic parse, named
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# entities, etc.
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doc = nlp.tokenizer(u'A unicode string, untokenized.')
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nlp.tagger.tag_from_strings([u'DT', u'JJ', u'NN', u',', u'VBN', u'.'])
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# Now predict dependency parse and named entities. Note that if you assign
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# tags in a way that's very unlike the behaviour of the POS tagger model,
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# the subsequent models may perform worse. These models use the POS tags
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# as features, so if you give them unexpected tags, you may be giving them
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# run-time conditions that don't resemble the training data.
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nlp.parser(doc)
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nlp.entity(doc)
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+code('python', 'Dependency Parse').
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import spacy
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from spacy.attrs import HEAD, DEP
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from spacy.symbols import det, nmod, root, punct
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from numpy import ndarray
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nlp = spacy.load('en')
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# Get the Doc object, and apply the pipeline except the dependency parser
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doc = nlp(u'A unicode string.', parse=False)
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columns = [HEAD, DEP]
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values = ndarray(shape=(len(columns), len(doc)), dtype='int32')
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# Syntactic parse specified as head offsets
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heads = [2, 1, 0, -1]
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# Integer IDs for the dependency labels. See the parse in the displaCy
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# demo at spacy.io/demos/displacy
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labels = [det, nmod, root, punct]
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values[0] = heads
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values[1] = labels
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doc.from_array(columns, values)
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+code('python', 'Named Entities').
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import spacy
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from spacy.attrs import ENT_TYPE, ENT_IOB
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from spacy.symbols import PERSON, ORG
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from numpy import ndarray
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nlp = spacy.load('en')
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# Get the Doc object, and apply the pipeline except the entity recognizer
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doc = nlp(u'My name is Matt.', entity=False)
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columns = [ENT_TYPE, ENT_IOB]
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values = ndarray(shape=(len(columns), len(doc)), dtype='int32')
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# IOB values are 0=missing, 1=I, 2=O, 3=B
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values[0] = [1, 1, 1, 3, 1]
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values[1] = [0, 0, 0, PERSON, 0]
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doc.from_array(columns, values)
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