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308 lines
11 KiB
Plaintext
308 lines
11 KiB
Plaintext
//- Docs > API > Span
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//- ============================================================================
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+section('span')
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+h2('span', 'https://github.com/' + profiles.github + '/spaCy/blob/master/spacy/tokens/span.pyx#L19')
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| #[+label('tag') class] Span
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p
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| A slice of a #[code Doc] object, consisting of zero or
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| more tokens. Spans are usually used to represent sentences, named entities,
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| phrases.
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+aside('Implementation')
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#[code Span] objects are views – that is, they do not copy the
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underlying C data. This makes them cheap to construct, as internally are
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simply a reference to the #[code Doc] object, a start position, an end
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position, and a label ID.
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+code('python', 'Overview').
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class Span:
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doc = Doc
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start = int
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end = int
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label = int
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def __init__(self, doc, start, end, label=0, vector=None, vector_norm=None):
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return self
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def __len__(self):
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return int
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def __getitem__(self, i):
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return Token()
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def __iter__(self):
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yield Token()
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def similarity(self, other):
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return float
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def merge(self, tag, lemma, ent_type):
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return None
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@property
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def label_(self):
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return unicode
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@property
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def vector(self):
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return numpy.ndarray(dtype='float64')
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@property
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def vector_norm(self):
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return float
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@property
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def text(self):
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return unicode
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@property
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def text_with_ws(self):
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return unicode
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@property
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def orth_(self):
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return unicode
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@property
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def lemma_(self):
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return unicode
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@property
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def root(self):
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return Token()
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@property
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def lefts(self):
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yield Token()
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@property
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def rights(self):
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yield Token()
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@property
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def subtree(self):
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yield Token()
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+section('span-create')
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+h3('span-init')
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| #[+label('tag') Section] Create a Span
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p
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| Span instances are usually created via the #[code Doc] object.
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+table(['Example', 'Description'], 'code')
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+row
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+cell #[code.lang-python span = doc[4 : 7]]
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+cell Produce a span with tokens 4, 5 and 6.
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+row
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+cell #[code.lang-python span = Span(doc, start, end, label=spacy.symbols.PERSON)]
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+cell Calling #[code Span.__init__] directly allows you to set a label.
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+row
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+cell #[code.lang-python for entity in doc.ents]
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+cell See #[a(href="/docs#doc-spans-ents") Doc.ents]
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+row
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+cell #[code.lang-python for sentence in doc.sents]
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+cell See #[a(href="/docs#doc-spans-sents") Doc.sents]
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+row
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+cell #[code.lang-python for noun_phrase in doc.noun_chunks]
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+cell See #[a(href="/docs#doc-spans-nounchunks") Doc.noun_chunks]
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+code('python', 'Definition').
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def __init__(self, doc, start, end, label=0, vector=None, vector_norm=None):
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return Span()
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+table(['Name', 'Type', 'Description'], 'params')
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+row
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+cell doc
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+cell Doc
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+cell The parent doc object, to slice from.
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+row
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+cell start
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+cell int
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+cell The index of the first token in the slice.
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+row
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+cell end
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+cell int
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+cell The index of the first token #[em outside] the slice (since ranges are exclusive in Python).
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+row
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+cell label
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+cell int or unicode
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+cell A label for the span. Either a string, or an integer ID, that should refer to a string mapped by the #[code Doc] object's #[code StringStore].
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+row
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+cell vector
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+cell
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+cell
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+row
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+cell vector_norm
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+cell
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+cell
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+section('span-merge')
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+h3('span-merge')
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| #[+label('tag') method] Span.merge
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p
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| Merge the span into a single token, modifying the underlying
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| #[code.lang-python Doc] object in place.
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+aside('Caveat').
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Magic is done to allow you to call #[code.lang-python merge()]
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without invalidating other #[code.lang-python Span] objects.
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However, it's difficult to ensure all indices are recomputed
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correctly. Please report any errors encountered on the issue
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tracker.
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+code('python', 'Example').
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for ent in doc.ents:
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ent.merge(ent.root.tag_, ent.text, ent.label_)
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for np in doc.noun_chunks:
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while len(np) > 1 and np[0].dep_ not in ('advmod', 'amod', 'compound'):
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np = np[1:]
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np.merge(np.root.tag_, np.text, np.root.ent_type_)
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+code('python', 'Definition').
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def merge(self, tag, lemma, ent_type):
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return None
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+table(['Name', 'Type', 'Description'], 'params')
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+row
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+cell tag
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+cell unicode
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+cell The fine-grained part-of-speech tag to assign to the new token.
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+row
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+cell lemma
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+cell unicode
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+cell The lemma string for the new token.
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+row
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+cell ent_type
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+cell unicode
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+cell The named entity type to assign to the new token.
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+section('span-similarity')
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+h3('span-similarity')
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| #[+label('tag') method] Span.similarity
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p
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| Estimate the semantic similarity between the span and another #[code Span],
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#[code Doc], #[code Token] or #[code Lexeme].
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+aside('Algorithm').
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Similarity is estimated
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using the cosine metric, between #[code Span.vector] and #[code other.vector].
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By default, #[code Span.vector] is computed by averaging the vectors
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of its tokens.
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+code('python', 'Example').
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doc = nlp("Apples and oranges are similar. Boots and hippos aren't.")
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apples_sent, boots_sent = doc.sents
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fruit = doc.vocab[u'fruit']
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assert apples_sent.similarity(fruit) > boot_sent.similarity(fruit)
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+code('python', 'Definition').
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def similarity(self, other):
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return float
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+table(['Name', 'Type', 'Description'], 'params')
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+row
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+cell other
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+cell Token, Span, Doc or Lexeme
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+cell The other object to judge similarity with.
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+section('span-sequence')
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+h3('span-sequence')
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| #[+label('tag') section] Span as a Sequence
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p.
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#[code Span] objects act as a sequence of #[code Token] objects. In
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this way they mirror the API of the #[code Doc] object.
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+table(['Name', 'Description'], 'params')
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+row
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+cell #[code.lang-python token = span[i]]
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+cell
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| Get the #[code Token] object at position #[em i], where
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| #[code i] is an offset within the #[code Span], not the
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| document. That is, if you have #[code.lang-python span = doc[4:6]],
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| then #[code.lang-python span[0].i == 4]
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+row
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+cell #[code.lang-python for token in span]
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+cell.
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Iterate over the #[code Token] objects in the span.
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+row
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+cell __len__
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+cell.
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Number of tokens in the span.
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+row
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+cell text
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+cell.
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The text content of the span, obtained from
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#[code ''.join(token.text_with_ws for token in span)].
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+row
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+cell start
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+cell.
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The start offset of the span, i.e. #[code span[0].i].
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+row
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+cell end
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+cell.
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The end offset of the span, i.e. #[code span[-1].i + 1].
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+section('span-navigating-parse')
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+h3('span-navigativing-parse')
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| #[+label('tag') Section] Span and the Syntactic Parse
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p.
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Span objects allow similar access to the syntactic parse as individual
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tokens.
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+table(['Name', 'Type', 'Description'], 'params')
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+row
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+cell root
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+cell #[code.lang-python Token]
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+cell
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| The word with the shortest path to the root of the sentence is
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| the root of the span.
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+row
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+cell lefts
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+cell #[code.lang-python yield Token]
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+cell Tokens that are to the left of the span, whose head is within it.
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+row
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+cell rights
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+cell #[code.lang-python yield Token]
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+cell Tokens that are to the right of the span, whose head is within it.
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+row
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+cell subtree
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+cell #[code.lang-python yield Token]
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+cell
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| Tokens in the range #[code (start, end+1)], where #[code start]
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| is the index of the leftmost word descended from a token in the
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| span, and #[code end] is the index of the rightmost token descended
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| from a token in the span.
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+section('span-strings')
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+h3('span-strings')
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| #[+label('tag') section] Span's Strings API
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p.
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You can access the textual content of the span, and different view of
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it, with the following properties.
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+table(['Name', 'Type', 'Description'], 'params')
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+row
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+cell text_with_ws
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+cell unicode
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+cell.
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The form of the span as it appears in the string, including
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trailing whitespace. This is useful when you need to use linguistic
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features to add inline mark-up to the string.
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+row
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+cell lemma / lemma_
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+cell int / unicode
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+cell.
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Whitespace-concatenated lemmas of each token in the span.
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+row
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+cell label / label_
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+cell int / unicode
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+cell.
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The span label, used particularly for named entities.
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