mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-31 07:57:35 +03:00
94e1346f44
* Change span lemmas to use original whitespace (fix #8368) This is a redo of #8371 based off master. The test for this required some changes to existing tests. I don't think the changes were significant but I'd like someone to check them. * Remove mystery docstring This sentence was uncompleted for years, and now we will never know how it ends.
790 lines
28 KiB
Cython
790 lines
28 KiB
Cython
from __future__ import unicode_literals
|
||
|
||
cimport numpy as np
|
||
from libc.math cimport sqrt
|
||
|
||
import numpy
|
||
from thinc.api import get_array_module
|
||
import warnings
|
||
import copy
|
||
|
||
from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
|
||
from ..structs cimport TokenC, LexemeC
|
||
from ..typedefs cimport flags_t, attr_t, hash_t
|
||
from ..attrs cimport attr_id_t
|
||
from ..parts_of_speech cimport univ_pos_t
|
||
from ..attrs cimport *
|
||
from ..lexeme cimport Lexeme
|
||
from ..symbols cimport dep
|
||
|
||
from ..util import normalize_slice
|
||
from ..errors import Errors, Warnings
|
||
from .underscore import Underscore, get_ext_args
|
||
|
||
|
||
cdef class Span:
|
||
"""A slice from a Doc object.
|
||
|
||
DOCS: https://spacy.io/api/span
|
||
"""
|
||
@classmethod
|
||
def set_extension(cls, name, **kwargs):
|
||
"""Define a custom attribute which becomes available as `Span._`.
|
||
|
||
name (str): Name of the attribute to set.
|
||
default: Optional default value of the attribute.
|
||
getter (callable): Optional getter function.
|
||
setter (callable): Optional setter function.
|
||
method (callable): Optional method for method extension.
|
||
force (bool): Force overwriting existing attribute.
|
||
|
||
DOCS: https://spacy.io/api/span#set_extension
|
||
USAGE: https://spacy.io/usage/processing-pipelines#custom-components-attributes
|
||
"""
|
||
if cls.has_extension(name) and not kwargs.get("force", False):
|
||
raise ValueError(Errors.E090.format(name=name, obj="Span"))
|
||
Underscore.span_extensions[name] = get_ext_args(**kwargs)
|
||
|
||
@classmethod
|
||
def get_extension(cls, name):
|
||
"""Look up a previously registered extension by name.
|
||
|
||
name (str): Name of the extension.
|
||
RETURNS (tuple): A `(default, method, getter, setter)` tuple.
|
||
|
||
DOCS: https://spacy.io/api/span#get_extension
|
||
"""
|
||
return Underscore.span_extensions.get(name)
|
||
|
||
@classmethod
|
||
def has_extension(cls, name):
|
||
"""Check whether an extension has been registered.
|
||
|
||
name (str): Name of the extension.
|
||
RETURNS (bool): Whether the extension has been registered.
|
||
|
||
DOCS: https://spacy.io/api/span#has_extension
|
||
"""
|
||
return name in Underscore.span_extensions
|
||
|
||
@classmethod
|
||
def remove_extension(cls, name):
|
||
"""Remove a previously registered extension.
|
||
|
||
name (str): Name of the extension.
|
||
RETURNS (tuple): A `(default, method, getter, setter)` tuple of the
|
||
removed extension.
|
||
|
||
DOCS: https://spacy.io/api/span#remove_extension
|
||
"""
|
||
if not cls.has_extension(name):
|
||
raise ValueError(Errors.E046.format(name=name))
|
||
return Underscore.span_extensions.pop(name)
|
||
|
||
def __cinit__(self, Doc doc, int start, int end, label=0, vector=None,
|
||
vector_norm=None, kb_id=0):
|
||
"""Create a `Span` object from the slice `doc[start : end]`.
|
||
|
||
doc (Doc): The parent document.
|
||
start (int): The index of the first token of the span.
|
||
end (int): The index of the first token after the span.
|
||
label (uint64): A label to attach to the Span, e.g. for named entities.
|
||
kb_id (uint64): An identifier from a Knowledge Base to capture the meaning of a named entity.
|
||
vector (ndarray[ndim=1, dtype='float32']): A meaning representation
|
||
of the span.
|
||
|
||
DOCS: https://spacy.io/api/span#init
|
||
"""
|
||
if not (0 <= start <= end <= len(doc)):
|
||
raise IndexError(Errors.E035.format(start=start, end=end, length=len(doc)))
|
||
self.doc = doc
|
||
if isinstance(label, str):
|
||
label = doc.vocab.strings.add(label)
|
||
if isinstance(kb_id, str):
|
||
kb_id = doc.vocab.strings.add(kb_id)
|
||
if label not in doc.vocab.strings:
|
||
raise ValueError(Errors.E084.format(label=label))
|
||
|
||
self.c = SpanC(
|
||
label=label,
|
||
kb_id=kb_id,
|
||
start=start,
|
||
end=end,
|
||
start_char=doc[start].idx if start < doc.length else 0,
|
||
end_char=doc[end - 1].idx + len(doc[end - 1]) if end >= 1 else 0,
|
||
)
|
||
self._vector = vector
|
||
self._vector_norm = vector_norm
|
||
|
||
def __richcmp__(self, Span other, int op):
|
||
if other is None:
|
||
if op == 0 or op == 1 or op == 2:
|
||
return False
|
||
else:
|
||
return True
|
||
# <
|
||
if op == 0:
|
||
return self.c.start_char < other.c.start_char
|
||
# <=
|
||
elif op == 1:
|
||
return self.c.start_char <= other.c.start_char
|
||
# ==
|
||
elif op == 2:
|
||
# Do the cheap comparisons first
|
||
return (
|
||
(self.c.start_char == other.c.start_char) and \
|
||
(self.c.end_char == other.c.end_char) and \
|
||
(self.c.label == other.c.label) and \
|
||
(self.c.kb_id == other.c.kb_id) and \
|
||
(self.doc == other.doc)
|
||
)
|
||
# !=
|
||
elif op == 3:
|
||
# Do the cheap comparisons first
|
||
return not (
|
||
(self.c.start_char == other.c.start_char) and \
|
||
(self.c.end_char == other.c.end_char) and \
|
||
(self.c.label == other.c.label) and \
|
||
(self.c.kb_id == other.c.kb_id) and \
|
||
(self.doc == other.doc)
|
||
)
|
||
# >
|
||
elif op == 4:
|
||
return self.c.start_char > other.c.start_char
|
||
# >=
|
||
elif op == 5:
|
||
return self.c.start_char >= other.c.start_char
|
||
|
||
def __hash__(self):
|
||
return hash((self.doc, self.c.start_char, self.c.end_char, self.c.label, self.c.kb_id))
|
||
|
||
def __len__(self):
|
||
"""Get the number of tokens in the span.
|
||
|
||
RETURNS (int): The number of tokens in the span.
|
||
|
||
DOCS: https://spacy.io/api/span#len
|
||
"""
|
||
if self.c.end < self.c.start:
|
||
return 0
|
||
return self.c.end - self.c.start
|
||
|
||
def __repr__(self):
|
||
return self.text
|
||
|
||
def __getitem__(self, object i):
|
||
"""Get a `Token` or a `Span` object
|
||
|
||
i (int or tuple): The index of the token within the span, or slice of
|
||
the span to get.
|
||
RETURNS (Token or Span): The token at `span[i]`.
|
||
|
||
DOCS: https://spacy.io/api/span#getitem
|
||
"""
|
||
if isinstance(i, slice):
|
||
start, end = normalize_slice(len(self), i.start, i.stop, i.step)
|
||
return Span(self.doc, start + self.start, end + self.start)
|
||
else:
|
||
if i < 0:
|
||
token_i = self.c.end + i
|
||
else:
|
||
token_i = self.c.start + i
|
||
if self.c.start <= token_i < self.c.end:
|
||
return self.doc[token_i]
|
||
else:
|
||
raise IndexError(Errors.E1002)
|
||
|
||
def __iter__(self):
|
||
"""Iterate over `Token` objects.
|
||
|
||
YIELDS (Token): A `Token` object.
|
||
|
||
DOCS: https://spacy.io/api/span#iter
|
||
"""
|
||
for i in range(self.c.start, self.c.end):
|
||
yield self.doc[i]
|
||
|
||
def __reduce__(self):
|
||
raise NotImplementedError(Errors.E112)
|
||
|
||
@property
|
||
def _(self):
|
||
"""Custom extension attributes registered via `set_extension`."""
|
||
return Underscore(Underscore.span_extensions, self,
|
||
start=self.c.start_char, end=self.c.end_char)
|
||
|
||
def as_doc(self, *, bint copy_user_data=False):
|
||
"""Create a `Doc` object with a copy of the `Span`'s data.
|
||
|
||
copy_user_data (bool): Whether or not to copy the original doc's user data.
|
||
RETURNS (Doc): The `Doc` copy of the span.
|
||
|
||
DOCS: https://spacy.io/api/span#as_doc
|
||
"""
|
||
words = [t.text for t in self]
|
||
spaces = [bool(t.whitespace_) for t in self]
|
||
cdef Doc doc = Doc(self.doc.vocab, words=words, spaces=spaces)
|
||
array_head = self.doc._get_array_attrs()
|
||
array = self.doc.to_array(array_head)
|
||
array = array[self.start : self.end]
|
||
self._fix_dep_copy(array_head, array)
|
||
# Fix initial IOB so the entities are valid for doc.ents below.
|
||
if len(array) > 0 and ENT_IOB in array_head:
|
||
ent_iob_col = array_head.index(ENT_IOB)
|
||
if array[0][ent_iob_col] == 1:
|
||
array[0][ent_iob_col] = 3
|
||
doc.from_array(array_head, array)
|
||
# Set partial entities at the beginning or end of the span to have
|
||
# missing entity annotation. Note: the initial partial entity could be
|
||
# detected from the IOB annotation but the final partial entity can't,
|
||
# so detect and remove both in the same way by checking self.ents.
|
||
span_ents = {(ent.start, ent.end) for ent in self.ents}
|
||
doc_ents = doc.ents
|
||
if len(doc_ents) > 0:
|
||
# Remove initial partial ent
|
||
if (doc_ents[0].start + self.start, doc_ents[0].end + self.start) not in span_ents:
|
||
doc.set_ents([], missing=[doc_ents[0]], default="unmodified")
|
||
# Remove final partial ent
|
||
if (doc_ents[-1].start + self.start, doc_ents[-1].end + self.start) not in span_ents:
|
||
doc.set_ents([], missing=[doc_ents[-1]], default="unmodified")
|
||
doc.noun_chunks_iterator = self.doc.noun_chunks_iterator
|
||
doc.user_hooks = self.doc.user_hooks
|
||
doc.user_span_hooks = self.doc.user_span_hooks
|
||
doc.user_token_hooks = self.doc.user_token_hooks
|
||
doc.vector = self.vector
|
||
doc.vector_norm = self.vector_norm
|
||
doc.tensor = self.doc.tensor[self.start : self.end]
|
||
for key, value in self.doc.cats.items():
|
||
if hasattr(key, "__len__") and len(key) == 3:
|
||
cat_start, cat_end, cat_label = key
|
||
if cat_start == self.start_char and cat_end == self.end_char:
|
||
doc.cats[cat_label] = value
|
||
if copy_user_data:
|
||
user_data = {}
|
||
char_offset = self.start_char
|
||
for key, value in self.doc.user_data.items():
|
||
if isinstance(key, tuple) and len(key) == 4 and key[0] == "._.":
|
||
data_type, name, start, end = key
|
||
if start is not None or end is not None:
|
||
start -= char_offset
|
||
if end is not None:
|
||
end -= char_offset
|
||
user_data[(data_type, name, start, end)] = copy.copy(value)
|
||
else:
|
||
user_data[key] = copy.copy(value)
|
||
doc.user_data = user_data
|
||
return doc
|
||
|
||
def _fix_dep_copy(self, attrs, array):
|
||
""" Rewire dependency links to make sure their heads fall into the span
|
||
while still keeping the correct number of sentences. """
|
||
cdef int length = len(array)
|
||
cdef attr_t value
|
||
cdef int i, head_col, ancestor_i
|
||
old_to_new_root = dict()
|
||
if HEAD in attrs:
|
||
head_col = attrs.index(HEAD)
|
||
for i in range(length):
|
||
# if the HEAD refers to a token outside this span, find a more appropriate ancestor
|
||
token = self[i]
|
||
ancestor_i = token.head.i - self.c.start # span offset
|
||
if ancestor_i not in range(length):
|
||
if DEP in attrs:
|
||
array[i, attrs.index(DEP)] = dep
|
||
|
||
# try finding an ancestor within this span
|
||
ancestors = token.ancestors
|
||
for ancestor in ancestors:
|
||
ancestor_i = ancestor.i - self.c.start
|
||
if ancestor_i in range(length):
|
||
array[i, head_col] = ancestor_i - i
|
||
|
||
# if there is no appropriate ancestor, define a new artificial root
|
||
value = array[i, head_col]
|
||
if (i+value) not in range(length):
|
||
new_root = old_to_new_root.get(ancestor_i, None)
|
||
if new_root is not None:
|
||
# take the same artificial root as a previous token from the same sentence
|
||
array[i, head_col] = new_root - i
|
||
else:
|
||
# set this token as the new artificial root
|
||
array[i, head_col] = 0
|
||
old_to_new_root[ancestor_i] = i
|
||
|
||
return array
|
||
|
||
def get_lca_matrix(self):
|
||
"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
|
||
`Span`, where LCA[i, j] is the index of the lowest common ancestor among
|
||
the tokens span[i] and span[j]. If they have no common ancestor within
|
||
the span, LCA[i, j] will be -1.
|
||
|
||
RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
|
||
(n, n), where n = len(self).
|
||
|
||
DOCS: https://spacy.io/api/span#get_lca_matrix
|
||
"""
|
||
return numpy.asarray(_get_lca_matrix(self.doc, self.c.start, self.c.end))
|
||
|
||
def similarity(self, other):
|
||
"""Make a semantic similarity estimate. The default estimate is cosine
|
||
similarity using an average of word vectors.
|
||
|
||
other (object): The object to compare with. By default, accepts `Doc`,
|
||
`Span`, `Token` and `Lexeme` objects.
|
||
RETURNS (float): A scalar similarity score. Higher is more similar.
|
||
|
||
DOCS: https://spacy.io/api/span#similarity
|
||
"""
|
||
if "similarity" in self.doc.user_span_hooks:
|
||
return self.doc.user_span_hooks["similarity"](self, other)
|
||
if len(self) == 1 and hasattr(other, "orth"):
|
||
if self[0].orth == other.orth:
|
||
return 1.0
|
||
elif isinstance(other, (Doc, Span)) and len(self) == len(other):
|
||
similar = True
|
||
for i in range(len(self)):
|
||
if self[i].orth != getattr(other[i], "orth", None):
|
||
similar = False
|
||
break
|
||
if similar:
|
||
return 1.0
|
||
if self.vocab.vectors.n_keys == 0:
|
||
warnings.warn(Warnings.W007.format(obj="Span"))
|
||
if self.vector_norm == 0.0 or other.vector_norm == 0.0:
|
||
warnings.warn(Warnings.W008.format(obj="Span"))
|
||
return 0.0
|
||
vector = self.vector
|
||
xp = get_array_module(vector)
|
||
return xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
|
||
|
||
cpdef np.ndarray to_array(self, object py_attr_ids):
|
||
"""Given a list of M attribute IDs, export the tokens to a numpy
|
||
`ndarray` of shape `(N, M)`, where `N` is the length of the document.
|
||
The values will be 32-bit integers.
|
||
|
||
attr_ids (list[int]): A list of attribute ID ints.
|
||
RETURNS (numpy.ndarray[long, ndim=2]): A feature matrix, with one row
|
||
per word, and one column per attribute indicated in the input
|
||
`attr_ids`.
|
||
"""
|
||
cdef int i, j
|
||
cdef attr_id_t feature
|
||
cdef np.ndarray[attr_t, ndim=2] output
|
||
# Make an array from the attributes - otherwise our inner loop is Python
|
||
# dict iteration
|
||
cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.uint64)
|
||
cdef int length = self.end - self.start
|
||
output = numpy.ndarray(shape=(length, len(attr_ids)), dtype=numpy.uint64)
|
||
for i in range(self.start, self.end):
|
||
for j, feature in enumerate(attr_ids):
|
||
output[i-self.start, j] = get_token_attr(&self.doc.c[i], feature)
|
||
return output
|
||
|
||
@property
|
||
def vocab(self):
|
||
"""RETURNS (Vocab): The Span's Doc's vocab."""
|
||
return self.doc.vocab
|
||
|
||
@property
|
||
def sent(self):
|
||
"""Obtain the sentence that contains this span. If the given span
|
||
crosses sentence boundaries, return only the first sentence
|
||
to which it belongs.
|
||
|
||
RETURNS (Span): The sentence span that the span is a part of.
|
||
"""
|
||
if "sent" in self.doc.user_span_hooks:
|
||
return self.doc.user_span_hooks["sent"](self)
|
||
# Use `sent_start` token attribute to find sentence boundaries
|
||
cdef int n = 0
|
||
if self.doc.has_annotation("SENT_START"):
|
||
# Find start of the sentence
|
||
start = self.start
|
||
while self.doc.c[start].sent_start != 1 and start > 0:
|
||
start += -1
|
||
# Find end of the sentence - can be within the entity
|
||
end = self.start + 1
|
||
while end < self.doc.length and self.doc.c[end].sent_start != 1:
|
||
end += 1
|
||
n += 1
|
||
if n >= self.doc.length:
|
||
break
|
||
return self.doc[start:end]
|
||
else:
|
||
raise ValueError(Errors.E030)
|
||
|
||
@property
|
||
def ents(self):
|
||
"""The named entities in the span. Returns a tuple of named entity
|
||
`Span` objects, if the entity recognizer has been applied.
|
||
|
||
RETURNS (tuple): Entities in the span, one `Span` per entity.
|
||
|
||
DOCS: https://spacy.io/api/span#ents
|
||
"""
|
||
cdef Span ent
|
||
ents = []
|
||
for ent in self.doc.ents:
|
||
if ent.c.start >= self.c.start:
|
||
if ent.c.end <= self.c.end:
|
||
ents.append(ent)
|
||
else:
|
||
break
|
||
return ents
|
||
|
||
@property
|
||
def has_vector(self):
|
||
"""A boolean value indicating whether a word vector is associated with
|
||
the object.
|
||
|
||
RETURNS (bool): Whether a word vector is associated with the object.
|
||
|
||
DOCS: https://spacy.io/api/span#has_vector
|
||
"""
|
||
if "has_vector" in self.doc.user_span_hooks:
|
||
return self.doc.user_span_hooks["has_vector"](self)
|
||
elif self.vocab.vectors.data.size > 0:
|
||
return any(token.has_vector for token in self)
|
||
elif self.doc.tensor.size > 0:
|
||
return True
|
||
else:
|
||
return False
|
||
|
||
@property
|
||
def vector(self):
|
||
"""A real-valued meaning representation. Defaults to an average of the
|
||
token vectors.
|
||
|
||
RETURNS (numpy.ndarray[ndim=1, dtype='float32']): A 1D numpy array
|
||
representing the span's semantics.
|
||
|
||
DOCS: https://spacy.io/api/span#vector
|
||
"""
|
||
if "vector" in self.doc.user_span_hooks:
|
||
return self.doc.user_span_hooks["vector"](self)
|
||
if self._vector is None:
|
||
self._vector = sum(t.vector for t in self) / len(self)
|
||
return self._vector
|
||
|
||
@property
|
||
def vector_norm(self):
|
||
"""The L2 norm of the span's vector representation.
|
||
|
||
RETURNS (float): The L2 norm of the vector representation.
|
||
|
||
DOCS: https://spacy.io/api/span#vector_norm
|
||
"""
|
||
if "vector_norm" in self.doc.user_span_hooks:
|
||
return self.doc.user_span_hooks["vector"](self)
|
||
vector = self.vector
|
||
xp = get_array_module(vector)
|
||
if self._vector_norm is None:
|
||
total = (vector*vector).sum()
|
||
self._vector_norm = xp.sqrt(total) if total != 0. else 0.
|
||
return self._vector_norm
|
||
|
||
@property
|
||
def tensor(self):
|
||
"""The span's slice of the doc's tensor.
|
||
|
||
RETURNS (ndarray[ndim=2, dtype='float32']): A 2D numpy or cupy array
|
||
representing the span's semantics.
|
||
"""
|
||
if self.doc.tensor is None:
|
||
return None
|
||
return self.doc.tensor[self.start : self.end]
|
||
|
||
@property
|
||
def sentiment(self):
|
||
"""RETURNS (float): A scalar value indicating the positivity or
|
||
negativity of the span.
|
||
"""
|
||
if "sentiment" in self.doc.user_span_hooks:
|
||
return self.doc.user_span_hooks["sentiment"](self)
|
||
else:
|
||
return sum([token.sentiment for token in self]) / len(self)
|
||
|
||
@property
|
||
def text(self):
|
||
"""RETURNS (str): The original verbatim text of the span."""
|
||
text = self.text_with_ws
|
||
if len(self) > 0 and self[-1].whitespace_:
|
||
text = text[:-1]
|
||
return text
|
||
|
||
@property
|
||
def text_with_ws(self):
|
||
"""The text content of the span with a trailing whitespace character if
|
||
the last token has one.
|
||
|
||
RETURNS (str): The text content of the span (with trailing
|
||
whitespace).
|
||
"""
|
||
return "".join([t.text_with_ws for t in self])
|
||
|
||
|
||
@property
|
||
def noun_chunks(self):
|
||
"""Iterate over the base noun phrases in the span. Yields base
|
||
noun-phrase #[code Span] objects, if the language has a noun chunk iterator.
|
||
Raises a NotImplementedError otherwise.
|
||
|
||
A base noun phrase, or "NP chunk", is a noun
|
||
phrase that does not permit other NPs to be nested within it – so no
|
||
NP-level coordination, no prepositional phrases, and no relative
|
||
clauses.
|
||
|
||
YIELDS (Span): Noun chunks in the span.
|
||
|
||
DOCS: https://spacy.io/api/span#noun_chunks
|
||
"""
|
||
for span in self.doc.noun_chunks:
|
||
if span.start >= self.start and span.end <= self.end:
|
||
yield span
|
||
|
||
@property
|
||
def root(self):
|
||
"""The token with the shortest path to the root of the
|
||
sentence (or the root itself). If multiple tokens are equally
|
||
high in the tree, the first token is taken.
|
||
|
||
RETURNS (Token): The root token.
|
||
|
||
DOCS: https://spacy.io/api/span#root
|
||
"""
|
||
if "root" in self.doc.user_span_hooks:
|
||
return self.doc.user_span_hooks["root"](self)
|
||
# This should probably be called 'head', and the other one called
|
||
# 'gov'. But we went with 'head' elsewhere, and now we're stuck =/
|
||
cdef int i
|
||
# First, we scan through the Span, and check whether there's a word
|
||
# with head==0, i.e. a sentence root. If so, we can return it. The
|
||
# longer the span, the more likely it contains a sentence root, and
|
||
# in this case we return in linear time.
|
||
for i in range(self.c.start, self.c.end):
|
||
if self.doc.c[i].head == 0:
|
||
return self.doc[i]
|
||
# If we don't have a sentence root, we do something that's not so
|
||
# algorithmically clever, but I think should be quite fast,
|
||
# especially for short spans.
|
||
# For each word, we count the path length, and arg min this measure.
|
||
# We could use better tree logic to save steps here...But I
|
||
# think this should be okay.
|
||
cdef int current_best = self.doc.length
|
||
cdef int root = -1
|
||
for i in range(self.c.start, self.c.end):
|
||
if self.c.start <= (i+self.doc.c[i].head) < self.c.end:
|
||
continue
|
||
words_to_root = _count_words_to_root(&self.doc.c[i], self.doc.length)
|
||
if words_to_root < current_best:
|
||
current_best = words_to_root
|
||
root = i
|
||
if root == -1:
|
||
return self.doc[self.c.start]
|
||
else:
|
||
return self.doc[root]
|
||
|
||
def char_span(self, int start_idx, int end_idx, label=0, kb_id=0, vector=None):
|
||
"""Create a `Span` object from the slice `span.text[start : end]`.
|
||
|
||
start (int): The index of the first character of the span.
|
||
end (int): The index of the first character after the span.
|
||
label (uint64 or string): A label to attach to the Span, e.g. for
|
||
named entities.
|
||
kb_id (uint64 or string): An ID from a KB to capture the meaning of a named entity.
|
||
vector (ndarray[ndim=1, dtype='float32']): A meaning representation of
|
||
the span.
|
||
RETURNS (Span): The newly constructed object.
|
||
"""
|
||
start_idx += self.c.start_char
|
||
end_idx += self.c.start_char
|
||
return self.doc.char_span(start_idx, end_idx, label=label, kb_id=kb_id, vector=vector)
|
||
|
||
@property
|
||
def conjuncts(self):
|
||
"""Tokens that are conjoined to the span's root.
|
||
|
||
RETURNS (tuple): A tuple of Token objects.
|
||
|
||
DOCS: https://spacy.io/api/span#lefts
|
||
"""
|
||
return self.root.conjuncts
|
||
|
||
@property
|
||
def lefts(self):
|
||
"""Tokens that are to the left of the span, whose head is within the
|
||
`Span`.
|
||
|
||
YIELDS (Token):A left-child of a token of the span.
|
||
|
||
DOCS: https://spacy.io/api/span#lefts
|
||
"""
|
||
for token in reversed(self): # Reverse, so we get tokens in order
|
||
for left in token.lefts:
|
||
if left.i < self.start:
|
||
yield left
|
||
|
||
@property
|
||
def rights(self):
|
||
"""Tokens that are to the right of the Span, whose head is within the
|
||
`Span`.
|
||
|
||
YIELDS (Token): A right-child of a token of the span.
|
||
|
||
DOCS: https://spacy.io/api/span#rights
|
||
"""
|
||
for token in self:
|
||
for right in token.rights:
|
||
if right.i >= self.end:
|
||
yield right
|
||
|
||
@property
|
||
def n_lefts(self):
|
||
"""The number of tokens that are to the left of the span, whose
|
||
heads are within the span.
|
||
|
||
RETURNS (int): The number of leftward immediate children of the
|
||
span, in the syntactic dependency parse.
|
||
|
||
DOCS: https://spacy.io/api/span#n_lefts
|
||
"""
|
||
return len(list(self.lefts))
|
||
|
||
@property
|
||
def n_rights(self):
|
||
"""The number of tokens that are to the right of the span, whose
|
||
heads are within the span.
|
||
|
||
RETURNS (int): The number of rightward immediate children of the
|
||
span, in the syntactic dependency parse.
|
||
|
||
DOCS: https://spacy.io/api/span#n_rights
|
||
"""
|
||
return len(list(self.rights))
|
||
|
||
@property
|
||
def subtree(self):
|
||
"""Tokens within the span and tokens which descend from them.
|
||
|
||
YIELDS (Token): A token within the span, or a descendant from it.
|
||
|
||
DOCS: https://spacy.io/api/span#subtree
|
||
"""
|
||
for word in self.lefts:
|
||
yield from word.subtree
|
||
yield from self
|
||
for word in self.rights:
|
||
yield from word.subtree
|
||
|
||
property start:
|
||
def __get__(self):
|
||
return self.c.start
|
||
|
||
def __set__(self, int start):
|
||
if start < 0:
|
||
raise IndexError("TODO")
|
||
self.c.start = start
|
||
|
||
property end:
|
||
def __get__(self):
|
||
return self.c.end
|
||
|
||
def __set__(self, int end):
|
||
if end < 0:
|
||
raise IndexError("TODO")
|
||
self.c.end = end
|
||
|
||
property start_char:
|
||
def __get__(self):
|
||
return self.c.start_char
|
||
|
||
def __set__(self, int start_char):
|
||
if start_char < 0:
|
||
raise IndexError("TODO")
|
||
self.c.start_char = start_char
|
||
|
||
property end_char:
|
||
def __get__(self):
|
||
return self.c.end_char
|
||
|
||
def __set__(self, int end_char):
|
||
if end_char < 0:
|
||
raise IndexError("TODO")
|
||
self.c.end_char = end_char
|
||
|
||
property label:
|
||
def __get__(self):
|
||
return self.c.label
|
||
|
||
def __set__(self, attr_t label):
|
||
self.c.label = label
|
||
|
||
property kb_id:
|
||
def __get__(self):
|
||
return self.c.kb_id
|
||
|
||
def __set__(self, attr_t kb_id):
|
||
self.c.kb_id = kb_id
|
||
|
||
property ent_id:
|
||
"""RETURNS (uint64): The entity ID."""
|
||
def __get__(self):
|
||
return self.root.ent_id
|
||
|
||
def __set__(self, hash_t key):
|
||
raise NotImplementedError(Errors.E200.format(attr="ent_id"))
|
||
|
||
property ent_id_:
|
||
"""RETURNS (str): The (string) entity ID."""
|
||
def __get__(self):
|
||
return self.root.ent_id_
|
||
|
||
def __set__(self, unicode key):
|
||
raise NotImplementedError(Errors.E200.format(attr="ent_id_"))
|
||
|
||
@property
|
||
def orth_(self):
|
||
"""Verbatim text content (identical to `Span.text`). Exists mostly for
|
||
consistency with other attributes.
|
||
|
||
RETURNS (str): The span's text."""
|
||
return self.text
|
||
|
||
@property
|
||
def lemma_(self):
|
||
"""RETURNS (str): The span's lemma."""
|
||
return "".join([t.lemma_ + t.whitespace_ for t in self]).strip()
|
||
|
||
property label_:
|
||
"""RETURNS (str): The span's label."""
|
||
def __get__(self):
|
||
return self.doc.vocab.strings[self.label]
|
||
|
||
def __set__(self, unicode label_):
|
||
self.label = self.doc.vocab.strings.add(label_)
|
||
|
||
property kb_id_:
|
||
"""RETURNS (str): The named entity's KB ID."""
|
||
def __get__(self):
|
||
return self.doc.vocab.strings[self.kb_id]
|
||
|
||
def __set__(self, unicode kb_id_):
|
||
self.kb_id = self.doc.vocab.strings.add(kb_id_)
|
||
|
||
|
||
cdef int _count_words_to_root(const TokenC* token, int sent_length) except -1:
|
||
# Don't allow spaces to be the root, if there are
|
||
# better candidates
|
||
if Lexeme.c_check_flag(token.lex, IS_SPACE) and token.l_kids == 0 and token.r_kids == 0:
|
||
return sent_length-1
|
||
if Lexeme.c_check_flag(token.lex, IS_PUNCT) and token.l_kids == 0 and token.r_kids == 0:
|
||
return sent_length-1
|
||
cdef int n = 0
|
||
while token.head != 0:
|
||
token += token.head
|
||
n += 1
|
||
if n >= sent_length:
|
||
raise RuntimeError(Errors.E039)
|
||
return n
|