spaCy/spacy/tokens/doc.pyx

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# cython: infer_types=True, bounds_check=False, profile=True
cimport cython
cimport numpy as np
from libc.string cimport memcpy, memset
from libc.math cimport sqrt
from libc.stdint cimport int32_t, uint64_t
import copy
from collections import Counter
import numpy
import numpy.linalg
import struct
import srsly
from thinc.api import get_array_module
from thinc.util import copy_array
import warnings
import copy
from .span cimport Span
from .token cimport Token
from ..lexeme cimport Lexeme, EMPTY_LEXEME
from ..typedefs cimport attr_t, flags_t
from ..attrs cimport ID, ORTH, NORM, LOWER, SHAPE, PREFIX, SUFFIX, CLUSTER
from ..attrs cimport LENGTH, POS, LEMMA, TAG, MORPH, DEP, HEAD, SPACY, ENT_IOB
from ..attrs cimport ENT_TYPE, ENT_ID, ENT_KB_ID, SENT_START, IDX, attr_id_t
from ..parts_of_speech cimport CCONJ, PUNCT, NOUN, univ_pos_t
from ..attrs import intify_attr, intify_attrs, IDS
from ..util import normalize_slice
from ..compat import copy_reg, pickle
from ..errors import Errors, Warnings
from .. import util
from .underscore import Underscore, get_ext_args
from ._retokenize import Retokenizer
DEF PADDING = 5
cdef int bounds_check(int i, int length, int padding) except -1:
if (i + padding) < 0:
raise IndexError(Errors.E026.format(i=i, length=length))
if (i - padding) >= length:
raise IndexError(Errors.E026.format(i=i, length=length))
cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil:
if feat_name == LEMMA:
return token.lemma
elif feat_name == NORM:
if not token.norm:
return token.lex.norm
return token.norm
elif feat_name == POS:
return token.pos
elif feat_name == TAG:
return token.tag
elif feat_name == MORPH:
return token.morph
elif feat_name == DEP:
return token.dep
elif feat_name == HEAD:
return token.head
elif feat_name == SENT_START:
return token.sent_start
elif feat_name == SPACY:
return token.spacy
elif feat_name == ENT_IOB:
return token.ent_iob
elif feat_name == ENT_TYPE:
return token.ent_type
elif feat_name == ENT_ID:
return token.ent_id
elif feat_name == ENT_KB_ID:
return token.ent_kb_id
elif feat_name == IDX:
return token.idx
else:
return Lexeme.get_struct_attr(token.lex, feat_name)
cdef attr_t get_token_attr_for_matcher(const TokenC* token, attr_id_t feat_name) nogil:
if feat_name == SENT_START:
if token.sent_start == 1:
return True
else:
return False
else:
return get_token_attr(token, feat_name)
cdef class Doc:
"""A sequence of Token objects. Access sentences and named entities, export
annotations to numpy arrays, losslessly serialize to compressed binary
strings. The `Doc` object holds an array of `TokenC` structs. The
Python-level `Token` and `Span` objects are views of this array, i.e.
they don't own the data themselves.
EXAMPLE:
Construction 1
>>> doc = nlp(u'Some text')
Construction 2
>>> from spacy.tokens import Doc
>>> doc = Doc(nlp.vocab, words=[u'hello', u'world', u'!'],
>>> spaces=[True, False, False])
DOCS: https://spacy.io/api/doc
"""
@classmethod
def set_extension(cls, name, **kwargs):
"""Define a custom attribute which becomes available as `Doc._`.
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/doc#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="Doc"))
Underscore.doc_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/doc#get_extension
"""
return Underscore.doc_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/doc#has_extension
"""
return name in Underscore.doc_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/doc#remove_extension
"""
if not cls.has_extension(name):
raise ValueError(Errors.E046.format(name=name))
return Underscore.doc_extensions.pop(name)
def __init__(self, Vocab vocab, words=None, spaces=None, user_data=None):
"""Create a Doc object.
vocab (Vocab): A vocabulary object, which must match any models you
want to use (e.g. tokenizer, parser, entity recognizer).
words (list or None): A list of unicode strings to add to the document
as words. If `None`, defaults to empty list.
spaces (list or None): A list of boolean values, of the same length as
words. True means that the word is followed by a space, False means
it is not. If `None`, defaults to `[True]*len(words)`
user_data (dict or None): Optional extra data to attach to the Doc.
DOCS: https://spacy.io/api/doc#init
"""
self.vocab = vocab
size = max(20, (len(words) if words is not None else 0))
self.mem = Pool()
# Guarantee self.lex[i-x], for any i >= 0 and x < padding is in bounds
# However, we need to remember the true starting places, so that we can
# realloc.
data_start = <TokenC*>self.mem.alloc(size + (PADDING*2), sizeof(TokenC))
cdef int i
for i in range(size + (PADDING*2)):
data_start[i].lex = &EMPTY_LEXEME
data_start[i].l_edge = i
data_start[i].r_edge = i
self.c = data_start + PADDING
self.max_length = size
self.length = 0
self.is_tagged = False
self.is_parsed = False
self.sentiment = 0.0
self.cats = {}
self.user_hooks = {}
self.user_token_hooks = {}
self.user_span_hooks = {}
self.tensor = numpy.zeros((0,), dtype="float32")
self.user_data = {} if user_data is None else user_data
self._vector = None
self.noun_chunks_iterator = self.vocab.get_noun_chunks
cdef bint has_space
if words is None and spaces is not None:
raise ValueError("words must be set if spaces is set")
elif spaces is None and words is not None:
self.has_unknown_spaces = True
else:
self.has_unknown_spaces = False
words = words if words is not None else []
spaces = spaces if spaces is not None else ([True] * len(words))
if len(spaces) != len(words):
raise ValueError(Errors.E027)
cdef const LexemeC* lexeme
for word, has_space in zip(words, spaces):
if isinstance(word, unicode):
lexeme = self.vocab.get(self.mem, word)
elif isinstance(word, bytes):
raise ValueError(Errors.E028.format(value=word))
else:
lexeme = self.vocab.get_by_orth(self.mem, word)
self.push_back(lexeme, has_space)
# Tough to decide on policy for this. Is an empty doc tagged and parsed?
# There's no information we'd like to add to it, so I guess so?
if self.length == 0:
self.is_tagged = True
self.is_parsed = True
@property
def _(self):
"""Custom extension attributes registered via `set_extension`."""
return Underscore(Underscore.doc_extensions, self)
@property
def is_sentenced(self):
"""Check if the document has sentence boundaries assigned. This is
defined as having at least one of the following:
a) An entry "sents" in doc.user_hooks";
b) Doc.is_parsed is set to True;
c) At least one token other than the first where sent_start is not None.
"""
if "sents" in self.user_hooks:
return True
if self.is_parsed:
return True
if len(self) < 2:
return True
for i in range(1, self.length):
if self.c[i].sent_start == -1 or self.c[i].sent_start == 1:
return True
return False
@property
def is_nered(self):
"""Check if the document has named entities set. Will return True if
*any* of the tokens has a named entity tag set (even if the others are
unknown values), or if the document is empty.
"""
if len(self) == 0:
return True
for i in range(self.length):
if self.c[i].ent_iob != 0:
return True
return False
def __getitem__(self, object i):
"""Get a `Token` or `Span` object.
i (int or tuple) The index of the token, or the slice of the document
to get.
RETURNS (Token or Span): The token at `doc[i]]`, or the span at
`doc[start : end]`.
EXAMPLE:
>>> doc[i]
Get the `Token` object at position `i`, where `i` is an integer.
Negative indexing is supported, and follows the usual Python
semantics, i.e. `doc[-2]` is `doc[len(doc) - 2]`.
>>> doc[start : end]]
Get a `Span` object, starting at position `start` and ending at
position `end`, where `start` and `end` are token indices. For
instance, `doc[2:5]` produces a span consisting of tokens 2, 3 and
4. Stepped slices (e.g. `doc[start : end : step]`) are not
supported, as `Span` objects must be contiguous (cannot have gaps).
You can use negative indices and open-ended ranges, which have
their normal Python semantics.
DOCS: https://spacy.io/api/doc#getitem
"""
if isinstance(i, slice):
start, stop = normalize_slice(len(self), i.start, i.stop, i.step)
return Span(self, start, stop, label=0)
if i < 0:
i = self.length + i
bounds_check(i, self.length, PADDING)
return Token.cinit(self.vocab, &self.c[i], i, self)
def __iter__(self):
"""Iterate over `Token` objects, from which the annotations can be
easily accessed. This is the main way of accessing `Token` objects,
which are the main way annotations are accessed from Python. If faster-
than-Python speeds are required, you can instead access the annotations
as a numpy array, or access the underlying C data directly from Cython.
DOCS: https://spacy.io/api/doc#iter
"""
cdef int i
for i in range(self.length):
yield Token.cinit(self.vocab, &self.c[i], i, self)
def __len__(self):
"""The number of tokens in the document.
RETURNS (int): The number of tokens in the document.
DOCS: https://spacy.io/api/doc#len
"""
return self.length
def __unicode__(self):
return "".join([t.text_with_ws for t in self])
def __bytes__(self):
return "".join([t.text_with_ws for t in self]).encode("utf-8")
def __str__(self):
return self.__unicode__()
def __repr__(self):
return self.__str__()
@property
def doc(self):
return self
def char_span(self, int start_idx, int end_idx, label=0, kb_id=0, vector=None):
"""Create a `Span` object from the slice `doc.text[start : end]`.
doc (Doc): The parent document.
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.
DOCS: https://spacy.io/api/doc#char_span
"""
if not isinstance(label, int):
label = self.vocab.strings.add(label)
if not isinstance(kb_id, int):
kb_id = self.vocab.strings.add(kb_id)
cdef int start = token_by_start(self.c, self.length, start_idx)
if start == -1:
return None
cdef int end = token_by_end(self.c, self.length, end_idx)
if end == -1:
return None
# Currently we have the token index, we want the range-end index
end += 1
cdef Span span = Span(self, start, end, label=label, kb_id=kb_id, vector=vector)
return span
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/doc#similarity
"""
if "similarity" in self.user_hooks:
return self.user_hooks["similarity"](self, other)
if isinstance(other, (Lexeme, Token)) and self.length == 1:
if self.c[0].lex.orth == other.orth:
return 1.0
elif isinstance(other, (Span, Doc)) and len(self) == len(other):
similar = True
for i in range(self.length):
if self[i].orth != other[i].orth:
similar = False
break
if similar:
return 1.0
if self.vocab.vectors.n_keys == 0:
warnings.warn(Warnings.W007.format(obj="Doc"))
if self.vector_norm == 0 or other.vector_norm == 0:
warnings.warn(Warnings.W008.format(obj="Doc"))
return 0.0
vector = self.vector
xp = get_array_module(vector)
result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
# ensure we get a scalar back (numpy does this automatically but cupy doesn't)
return result.item()
@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/doc#has_vector
"""
if "has_vector" in self.user_hooks:
return self.user_hooks["has_vector"](self)
elif self.vocab.vectors.data.size:
return True
elif self.tensor.size:
return True
else:
return False
property vector:
"""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 document's semantics.
DOCS: https://spacy.io/api/doc#vector
"""
def __get__(self):
if "vector" in self.user_hooks:
return self.user_hooks["vector"](self)
if self._vector is not None:
return self._vector
xp = get_array_module(self.vocab.vectors.data)
if not len(self):
self._vector = xp.zeros((self.vocab.vectors_length,), dtype="f")
return self._vector
elif self.vocab.vectors.data.size > 0:
self._vector = sum(t.vector for t in self) / len(self)
return self._vector
elif self.tensor.size > 0:
self._vector = self.tensor.mean(axis=0)
return self._vector
else:
return xp.zeros((self.vocab.vectors_length,), dtype="float32")
def __set__(self, value):
self._vector = value
property vector_norm:
"""The L2 norm of the document's vector representation.
RETURNS (float): The L2 norm of the vector representation.
DOCS: https://spacy.io/api/doc#vector_norm
"""
def __get__(self):
if "vector_norm" in self.user_hooks:
return self.user_hooks["vector_norm"](self)
cdef float value
cdef double norm = 0
if self._vector_norm is None:
norm = 0.0
for value in self.vector:
norm += value * value
self._vector_norm = sqrt(norm) if norm != 0 else 0
return self._vector_norm
def __set__(self, value):
self._vector_norm = value
@property
def text(self):
"""A unicode representation of the document text.
RETURNS (str): The original verbatim text of the document.
"""
return "".join(t.text_with_ws for t in self)
@property
def text_with_ws(self):
"""An alias of `Doc.text`, provided for duck-type compatibility with
`Span` and `Token`.
RETURNS (str): The original verbatim text of the document.
"""
return self.text
property ents:
"""The named entities in the document. Returns a tuple of named entity
`Span` objects, if the entity recognizer has been applied.
RETURNS (tuple): Entities in the document, one `Span` per entity.
DOCS: https://spacy.io/api/doc#ents
"""
def __get__(self):
cdef int i
cdef const TokenC* token
cdef int start = -1
cdef attr_t label = 0
cdef attr_t kb_id = 0
output = []
for i in range(self.length):
token = &self.c[i]
if token.ent_iob == 1:
if start == -1:
seq = [f"{t.text}|{t.ent_iob_}" for t in self[i-5:i+5]]
raise ValueError(Errors.E093.format(seq=" ".join(seq)))
elif token.ent_iob == 2 or token.ent_iob == 0 or \
(token.ent_iob == 3 and token.ent_type == 0):
if start != -1:
output.append(Span(self, start, i, label=label, kb_id=kb_id))
start = -1
label = 0
kb_id = 0
elif token.ent_iob == 3:
if start != -1:
output.append(Span(self, start, i, label=label, kb_id=kb_id))
start = i
label = token.ent_type
kb_id = token.ent_kb_id
if start != -1:
output.append(Span(self, start, self.length, label=label, kb_id=kb_id))
# remove empty-label spans
output = [o for o in output if o.label_ != ""]
return tuple(output)
def __set__(self, ents):
# TODO:
# 1. Test basic data-driven ORTH gazetteer
# 2. Test more nuanced date and currency regex
tokens_in_ents = {}
cdef attr_t entity_type
cdef attr_t kb_id
cdef int ent_start, ent_end
for ent_info in ents:
entity_type, kb_id, ent_start, ent_end = get_entity_info(ent_info)
for token_index in range(ent_start, ent_end):
if token_index in tokens_in_ents.keys():
raise ValueError(Errors.E103.format(
span1=(tokens_in_ents[token_index][0],
tokens_in_ents[token_index][1],
self.vocab.strings[tokens_in_ents[token_index][2]]),
span2=(ent_start, ent_end, self.vocab.strings[entity_type])))
tokens_in_ents[token_index] = (ent_start, ent_end, entity_type, kb_id)
cdef int i
for i in range(self.length):
# default values
entity_type = 0
kb_id = 0
# Set ent_iob to Missing (0) bij default unless this token was nered before
ent_iob = 0
if self.c[i].ent_iob != 0:
ent_iob = 2
# overwrite if the token was part of a specified entity
if i in tokens_in_ents.keys():
ent_start, ent_end, entity_type, kb_id = tokens_in_ents[i]
if entity_type is None or entity_type <= 0:
# Blocking this token from being overwritten by downstream NER
ent_iob = 3
elif ent_start == i:
# Marking the start of an entity
ent_iob = 3
else:
# Marking the inside of an entity
ent_iob = 1
self.c[i].ent_type = entity_type
self.c[i].ent_kb_id = kb_id
self.c[i].ent_iob = ent_iob
@property
def noun_chunks(self):
"""Iterate over the base noun phrases in the document. Yields base
noun-phrase #[code Span] objects, if the document has been
syntactically parsed. 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 document.
DOCS: https://spacy.io/api/doc#noun_chunks
"""
# Accumulate the result before beginning to iterate over it. This
# prevents the tokenisation from being changed out from under us
# during the iteration. The tricky thing here is that Span accepts
# its tokenisation changing, so it's okay once we have the Span
# objects. See Issue #375.
spans = []
if self.noun_chunks_iterator is not None:
for start, end, label in self.noun_chunks_iterator(self):
spans.append(Span(self, start, end, label=label))
for span in spans:
yield span
@property
def sents(self):
"""Iterate over the sentences in the document. Yields sentence `Span`
objects. Sentence spans have no label. To improve accuracy on informal
texts, spaCy calculates sentence boundaries from the syntactic
dependency parse. If the parser is disabled, the `sents` iterator will
be unavailable.
YIELDS (Span): Sentences in the document.
DOCS: https://spacy.io/api/doc#sents
"""
if not self.is_sentenced:
raise ValueError(Errors.E030)
if "sents" in self.user_hooks:
yield from self.user_hooks["sents"](self)
else:
start = 0
for i in range(1, self.length):
if self.c[i].sent_start == 1:
yield Span(self, start, i)
start = i
if start != self.length:
yield Span(self, start, self.length)
@property
def lang(self):
"""RETURNS (uint64): ID of the language of the doc's vocabulary."""
return self.vocab.strings[self.vocab.lang]
@property
def lang_(self):
"""RETURNS (str): Language of the doc's vocabulary, e.g. 'en'."""
return self.vocab.lang
cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1:
if self.length == 0:
# Flip these to false when we see the first token.
self.is_tagged = False
self.is_parsed = False
if self.length == self.max_length:
self._realloc(self.length * 2)
cdef TokenC* t = &self.c[self.length]
if LexemeOrToken is const_TokenC_ptr:
t[0] = lex_or_tok[0]
else:
t.lex = lex_or_tok
if self.length == 0:
t.idx = 0
else:
t.idx = (t-1).idx + (t-1).lex.length + (t-1).spacy
t.l_edge = self.length
t.r_edge = self.length
if t.lex.orth == 0:
raise ValueError(Errors.E031.format(i=self.length))
t.spacy = has_space
self.length += 1
if self.length == 1:
# Set token.sent_start to 1 for first token. See issue #2869
self.c[0].sent_start = 1
return t.idx + t.lex.length + t.spacy
@cython.boundscheck(False)
cpdef np.ndarray to_array(self, object py_attr_ids):
"""Export given token attributes to a numpy `ndarray`.
If `attr_ids` is a sequence of M attributes, the output array will be
of shape `(N, M)`, where N is the length of the `Doc` (in tokens). If
`attr_ids` is a single attribute, the output shape will be (N,). You
can specify attributes by integer ID (e.g. spacy.attrs.LEMMA) or
string name (e.g. 'LEMMA' or 'lemma').
attr_ids (list[]): A list of attributes (int IDs or string names).
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`.
EXAMPLE:
>>> from spacy.attrs import LOWER, POS, ENT_TYPE, IS_ALPHA
>>> doc = nlp(text)
>>> # All strings mapped to integers, for easy export to numpy
>>> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
"""
cdef int i, j
cdef attr_id_t feature
cdef np.ndarray[attr_t, ndim=2] output
# Handle scalar/list inputs of strings/ints for py_attr_ids
# See also #3064
if isinstance(py_attr_ids, str):
# Handle inputs like doc.to_array('ORTH')
py_attr_ids = [py_attr_ids]
elif not hasattr(py_attr_ids, "__iter__"):
# Handle inputs like doc.to_array(ORTH)
py_attr_ids = [py_attr_ids]
# Allow strings, e.g. 'lemma' or 'LEMMA'
try:
py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
for id_ in py_attr_ids]
except KeyError as msg:
keys = [k for k in IDS.keys() if not k.startswith("FLAG")]
raise KeyError(Errors.E983.format(dict="IDS", key=msg, keys=keys)) from None
# Make an array from the attributes --- otherwise our inner loop is
# Python dict iteration.
cdef np.ndarray attr_ids = numpy.asarray(py_attr_ids, dtype="i")
output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.uint64)
c_output = <attr_t*>output.data
c_attr_ids = <attr_id_t*>attr_ids.data
cdef TokenC* token
cdef int nr_attr = attr_ids.shape[0]
for i in range(self.length):
token = &self.c[i]
for j in range(nr_attr):
c_output[i*nr_attr + j] = get_token_attr(token, c_attr_ids[j])
# Handle 1d case
return output if len(attr_ids) >= 2 else output.reshape((self.length,))
def count_by(self, attr_id_t attr_id, exclude=None, object counts=None):
"""Count the frequencies of a given attribute. Produces a dict of
`{attribute (int): count (ints)}` frequencies, keyed by the values of
the given attribute ID.
attr_id (int): The attribute ID to key the counts.
RETURNS (dict): A dictionary mapping attributes to integer counts.
DOCS: https://spacy.io/api/doc#count_by
"""
cdef int i
cdef attr_t attr
cdef size_t count
if counts is None:
counts = Counter()
output_dict = True
else:
output_dict = False
# Take this check out of the loop, for a bit of extra speed
if exclude is None:
for i in range(self.length):
counts[get_token_attr(&self.c[i], attr_id)] += 1
else:
for i in range(self.length):
if not exclude(self[i]):
counts[get_token_attr(&self.c[i], attr_id)] += 1
if output_dict:
return dict(counts)
def _realloc(self, new_size):
if new_size < self.max_length:
return
self.max_length = new_size
n = new_size + (PADDING * 2)
# What we're storing is a "padded" array. We've jumped forward PADDING
# places, and are storing the pointer to that. This way, we can access
# words out-of-bounds, and get out-of-bounds markers.
# Now that we want to realloc, we need the address of the true start,
# so we jump the pointer back PADDING places.
cdef TokenC* data_start = self.c - PADDING
data_start = <TokenC*>self.mem.realloc(data_start, n * sizeof(TokenC))
self.c = data_start + PADDING
cdef int i
for i in range(self.length, self.max_length + PADDING):
self.c[i].lex = &EMPTY_LEXEME
cdef void set_parse(self, const TokenC* parsed) nogil:
# TODO: This method is fairly misleading atm. It's used by Parser
# to actually apply the parse calculated. Need to rethink this.
# Probably we should use from_array?
self.is_parsed = True
for i in range(self.length):
self.c[i] = parsed[i]
def from_array(self, attrs, array):
"""Load attributes from a numpy array. Write to a `Doc` object, from an
`(M, N)` array of attributes.
attrs (list) A list of attribute ID ints.
array (numpy.ndarray[ndim=2, dtype='int32']): The attribute values.
RETURNS (Doc): Itself.
DOCS: https://spacy.io/api/doc#from_array
"""
# Handle scalar/list inputs of strings/ints for py_attr_ids
# See also #3064
if isinstance(attrs, str):
# Handle inputs like doc.to_array('ORTH')
attrs = [attrs]
elif not hasattr(attrs, "__iter__"):
# Handle inputs like doc.to_array(ORTH)
attrs = [attrs]
# Allow strings, e.g. 'lemma' or 'LEMMA'
attrs = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
for id_ in attrs]
if array.dtype != numpy.uint64:
warnings.warn(Warnings.W028.format(type=array.dtype))
if SENT_START in attrs and HEAD in attrs:
raise ValueError(Errors.E032)
cdef int i, col
cdef int32_t abs_head_index
cdef attr_id_t attr_id
cdef TokenC* tokens = self.c
cdef int length = len(array)
if length != len(self):
raise ValueError(Errors.E971.format(array_length=length, doc_length=len(self)))
# Get set up for fast loading
cdef Pool mem = Pool()
cdef int n_attrs = len(attrs)
# attrs should not be empty, but make sure to avoid zero-length mem alloc
assert n_attrs > 0
attr_ids = <attr_id_t*>mem.alloc(n_attrs, sizeof(attr_id_t))
for i, attr_id in enumerate(attrs):
attr_ids[i] = attr_id
if len(array.shape) == 1:
array = array.reshape((array.size, 1))
cdef np.ndarray transposed_array = numpy.ascontiguousarray(array.T)
values = <const uint64_t*>transposed_array.data
stride = transposed_array.shape[1]
# Check that all heads are within the document bounds
if HEAD in attrs:
col = attrs.index(HEAD)
for i in range(length):
# cast index to signed int
abs_head_index = <int32_t>values[col * stride + i]
abs_head_index += i
if abs_head_index < 0 or abs_head_index >= length:
raise ValueError(
Errors.E190.format(
index=i,
value=array[i, col],
rel_head_index=abs_head_index-i
)
)
# Verify ENT_IOB are proper integers
if ENT_IOB in attrs:
iob_strings = Token.iob_strings()
col = attrs.index(ENT_IOB)
n_iob_strings = len(iob_strings)
for i in range(length):
value = values[col * stride + i]
if value < 0 or value >= n_iob_strings:
raise ValueError(
Errors.E982.format(
values=iob_strings,
value=value
)
)
# Now load the data
for i in range(length):
token = &self.c[i]
for j in range(n_attrs):
value = values[j * stride + i]
if attr_ids[j] == MORPH:
# add morph to morphology table
self.vocab.morphology.add(self.vocab.strings[value])
Token.set_struct_attr(token, attr_ids[j], value)
# Set flags
self.is_parsed = bool(self.is_parsed or HEAD in attrs)
self.is_tagged = bool(self.is_tagged or TAG in attrs or POS in attrs)
# If document is parsed, set children
if self.is_parsed:
set_children_from_heads(self.c, length)
return self
@staticmethod
def from_docs(docs, ensure_whitespace=True, attrs=None):
"""Concatenate multiple Doc objects to form a new one. Raises an error if the `Doc` objects do not all share
the same `Vocab`.
docs (list): A list of Doc objects.
ensure_whitespace (bool): Insert a space between two adjacent docs whenever the first doc does not end in whitespace.
attrs (list): Optional list of attribute ID ints or attribute name strings.
RETURNS (Doc): A doc that contains the concatenated docs, or None if no docs were given.
DOCS: https://spacy.io/api/doc#from_docs
"""
if not docs:
return None
vocab = {doc.vocab for doc in docs}
if len(vocab) > 1:
raise ValueError(Errors.E999)
(vocab,) = vocab
if attrs is None:
attrs = [LEMMA, NORM]
if all(doc.is_nered for doc in docs):
attrs.extend([ENT_IOB, ENT_KB_ID, ENT_TYPE])
# TODO: separate for is_morphed?
if all(doc.is_tagged for doc in docs):
attrs.extend([TAG, POS, MORPH])
if all(doc.is_parsed for doc in docs):
attrs.extend([HEAD, DEP])
else:
attrs.append(SENT_START)
else:
if any(isinstance(attr, str) for attr in attrs): # resolve attribute names
attrs = [intify_attr(attr) for attr in attrs] # intify_attr returns None for invalid attrs
attrs = list(attr for attr in set(attrs) if attr) # filter duplicates, remove None if present
if SPACY not in attrs:
attrs.append(SPACY)
concat_words = []
concat_spaces = []
concat_user_data = {}
char_offset = 0
for doc in docs:
concat_words.extend(t.text for t in doc)
concat_spaces.extend(bool(t.whitespace_) for t in doc)
for key, value in doc.user_data.items():
if isinstance(key, tuple) and len(key) == 4:
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
concat_user_data[(data_type, name, start, end)] = copy.copy(value)
else:
warnings.warn(Warnings.W101.format(name=name))
else:
warnings.warn(Warnings.W102.format(key=key, value=value))
char_offset += len(doc.text) if not ensure_whitespace or doc[-1].is_space else len(doc.text) + 1
arrays = [doc.to_array(attrs) for doc in docs]
if ensure_whitespace:
spacy_index = attrs.index(SPACY)
for i, array in enumerate(arrays[:-1]):
if len(array) > 0 and not docs[i][-1].is_space:
array[-1][spacy_index] = 1
token_offset = -1
for doc in docs[:-1]:
token_offset += len(doc)
if not doc[-1].is_space:
concat_spaces[token_offset] = True
concat_array = numpy.concatenate(arrays)
concat_doc = Doc(vocab, words=concat_words, spaces=concat_spaces, user_data=concat_user_data)
concat_doc.from_array(attrs, concat_array)
return concat_doc
def get_lca_matrix(self):
"""Calculates a matrix of Lowest Common Ancestors (LCA) for a given
`Doc`, where LCA[i, j] is the index of the lowest common ancestor among
token i and j.
RETURNS (np.array[ndim=2, dtype=numpy.int32]): LCA matrix with shape
(n, n), where n = len(self).
DOCS: https://spacy.io/api/doc#get_lca_matrix
"""
return numpy.asarray(_get_lca_matrix(self, 0, len(self)))
def copy(self):
cdef Doc other = Doc(self.vocab)
other._vector = copy.deepcopy(self._vector)
other._vector_norm = copy.deepcopy(self._vector_norm)
other.tensor = copy.deepcopy(self.tensor)
other.cats = copy.deepcopy(self.cats)
other.user_data = copy.deepcopy(self.user_data)
other.is_tagged = self.is_tagged
other.is_parsed = self.is_parsed
other.is_morphed = self.is_morphed
other.sentiment = self.sentiment
other.has_unknown_spaces = self.has_unknown_spaces
other.user_hooks = dict(self.user_hooks)
other.user_token_hooks = dict(self.user_token_hooks)
other.user_span_hooks = dict(self.user_span_hooks)
other.length = self.length
other.max_length = self.max_length
buff_size = other.max_length + (PADDING*2)
tokens = <TokenC*>other.mem.alloc(buff_size, sizeof(TokenC))
memcpy(tokens, self.c - PADDING, buff_size * sizeof(TokenC))
other.c = &tokens[PADDING]
return other
def to_disk(self, path, *, exclude=tuple()):
"""Save the current state to a directory.
path (str / Path): A path to a directory, which will be created if
it doesn't exist. Paths may be either strings or Path-like objects.
exclude (Iterable[str]): String names of serialization fields to exclude.
DOCS: https://spacy.io/api/doc#to_disk
"""
path = util.ensure_path(path)
with path.open("wb") as file_:
file_.write(self.to_bytes(exclude=exclude))
def from_disk(self, path, *, exclude=tuple()):
"""Loads state from a directory. Modifies the object in place and
returns it.
path (str / Path): A path to a directory. Paths may be either
strings or `Path`-like objects.
exclude (list): String names of serialization fields to exclude.
RETURNS (Doc): The modified `Doc` object.
DOCS: https://spacy.io/api/doc#from_disk
"""
path = util.ensure_path(path)
with path.open("rb") as file_:
bytes_data = file_.read()
return self.from_bytes(bytes_data, exclude=exclude)
def to_bytes(self, *, exclude=tuple()):
"""Serialize, i.e. export the document contents to a binary string.
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
all annotations.
DOCS: https://spacy.io/api/doc#to_bytes
"""
return srsly.msgpack_dumps(self.to_dict(exclude=exclude))
def from_bytes(self, bytes_data, *, exclude=tuple()):
"""Deserialize, i.e. import the document contents from a binary string.
data (bytes): The string to load from.
exclude (list): String names of serialization fields to exclude.
RETURNS (Doc): Itself.
DOCS: https://spacy.io/api/doc#from_bytes
"""
return self.from_dict(srsly.msgpack_loads(bytes_data), exclude=exclude)
def to_dict(self, *, exclude=tuple()):
"""Export the document contents to a dictionary for serialization.
exclude (list): String names of serialization fields to exclude.
RETURNS (bytes): A losslessly serialized copy of the `Doc`, including
all annotations.
DOCS: https://spacy.io/api/doc#to_bytes
"""
array_head = [LENGTH, SPACY, LEMMA, ENT_IOB, ENT_TYPE, ENT_ID, NORM, ENT_KB_ID]
if self.is_tagged:
array_head.extend([TAG, POS])
# If doc parsed add head and dep attribute
if self.is_parsed:
array_head.extend([HEAD, DEP])
# Otherwise add sent_start
else:
array_head.append(SENT_START)
strings = set()
for token in self:
strings.add(token.tag_)
strings.add(token.lemma_)
strings.add(token.dep_)
strings.add(token.ent_type_)
strings.add(token.ent_kb_id_)
strings.add(token.norm_)
# Msgpack doesn't distinguish between lists and tuples, which is
# vexing for user data. As a best guess, we *know* that within
# keys, we must have tuples. In values we just have to hope
# users don't mind getting a list instead of a tuple.
serializers = {
"text": lambda: self.text,
"array_head": lambda: array_head,
"array_body": lambda: self.to_array(array_head),
"sentiment": lambda: self.sentiment,
"tensor": lambda: self.tensor,
"cats": lambda: self.cats,
"strings": lambda: list(strings),
"has_unknown_spaces": lambda: self.has_unknown_spaces
}
if "user_data" not in exclude and self.user_data:
user_data_keys, user_data_values = list(zip(*self.user_data.items()))
if "user_data_keys" not in exclude:
serializers["user_data_keys"] = lambda: srsly.msgpack_dumps(user_data_keys)
if "user_data_values" not in exclude:
serializers["user_data_values"] = lambda: srsly.msgpack_dumps(user_data_values)
return util.to_dict(serializers, exclude)
def from_dict(self, msg, *, exclude=tuple()):
"""Deserialize, i.e. import the document contents from a binary string.
data (bytes): The string to load from.
exclude (list): String names of serialization fields to exclude.
RETURNS (Doc): Itself.
DOCS: https://spacy.io/api/doc#from_dict
"""
if self.length != 0:
raise ValueError(Errors.E033.format(length=self.length))
deserializers = {
"text": lambda b: None,
"array_head": lambda b: None,
"array_body": lambda b: None,
"sentiment": lambda b: None,
"tensor": lambda b: None,
"cats": lambda b: None,
"strings": lambda b: None,
"user_data_keys": lambda b: None,
"user_data_values": lambda b: None,
"has_unknown_spaces": lambda b: None
}
# Msgpack doesn't distinguish between lists and tuples, which is
# vexing for user data. As a best guess, we *know* that within
# keys, we must have tuples. In values we just have to hope
# users don't mind getting a list instead of a tuple.
if "user_data" not in exclude and "user_data_keys" in msg:
user_data_keys = srsly.msgpack_loads(msg["user_data_keys"], use_list=False)
user_data_values = srsly.msgpack_loads(msg["user_data_values"])
for key, value in zip(user_data_keys, user_data_values):
self.user_data[key] = value
cdef int i, start, end, has_space
if "sentiment" not in exclude and "sentiment" in msg:
self.sentiment = msg["sentiment"]
if "tensor" not in exclude and "tensor" in msg:
self.tensor = msg["tensor"]
if "cats" not in exclude and "cats" in msg:
self.cats = msg["cats"]
if "strings" not in exclude and "strings" in msg:
for s in msg["strings"]:
self.vocab.strings.add(s)
if "has_unknown_spaces" not in exclude and "has_unknown_spaces" in msg:
self.has_unknown_spaces = msg["has_unknown_spaces"]
start = 0
cdef const LexemeC* lex
cdef unicode orth_
text = msg["text"]
attrs = msg["array_body"]
for i in range(attrs.shape[0]):
end = start + attrs[i, 0]
has_space = attrs[i, 1]
orth_ = text[start:end]
lex = self.vocab.get(self.mem, orth_)
self.push_back(lex, has_space)
start = end + has_space
self.from_array(msg["array_head"][2:], attrs[:, 2:])
return self
def extend_tensor(self, tensor):
"""Concatenate a new tensor onto the doc.tensor object.
The doc.tensor attribute holds dense feature vectors
computed by the models in the pipeline. Let's say a
document with 30 words has a tensor with 128 dimensions
per word. doc.tensor.shape will be (30, 128). After
calling doc.extend_tensor with an array of shape (30, 64),
doc.tensor == (30, 192).
"""
xp = get_array_module(self.tensor)
if self.tensor.size == 0:
self.tensor.resize(tensor.shape, refcheck=False)
copy_array(self.tensor, tensor)
else:
self.tensor = xp.hstack((self.tensor, tensor))
def retokenize(self):
"""Context manager to handle retokenization of the Doc.
Modifications to the Doc's tokenization are stored, and then
made all at once when the context manager exits. This is
much more efficient, and less error-prone.
All views of the Doc (Span and Token) created before the
retokenization are invalidated, although they may accidentally
continue to work.
DOCS: https://spacy.io/api/doc#retokenize
USAGE: https://spacy.io/usage/linguistic-features#retokenization
"""
return Retokenizer(self)
def _bulk_merge(self, spans, attributes):
"""Retokenize the document, such that the spans given as arguments
are merged into single tokens. The spans need to be in document
order, and no span intersection is allowed.
spans (Span[]): Spans to merge, in document order, with all span
intersections empty. Cannot be empty.
attributes (Dictionary[]): Attributes to assign to the merged tokens. By default,
must be the same length as spans, empty dictionaries are allowed.
attributes are inherited from the syntactic root of the span.
RETURNS (Token): The first newly merged token.
"""
cdef unicode tag, lemma, ent_type
attr_len = len(attributes)
span_len = len(spans)
if not attr_len == span_len:
raise ValueError(Errors.E121.format(attr_len=attr_len, span_len=span_len))
with self.retokenize() as retokenizer:
for i, span in enumerate(spans):
fix_attributes(self, attributes[i])
remove_label_if_necessary(attributes[i])
retokenizer.merge(span, attributes[i])
def to_json(self, underscore=None):
"""Convert a Doc to JSON. The format it produces will be the new format
for the `spacy train` command (not implemented yet).
underscore (list): Optional list of string names of custom doc._.
attributes. Attribute values need to be JSON-serializable. Values will
be added to an "_" key in the data, e.g. "_": {"foo": "bar"}.
RETURNS (dict): The data in spaCy's JSON format.
DOCS: https://spacy.io/api/doc#to_json
"""
data = {"text": self.text}
if self.is_nered:
data["ents"] = [{"start": ent.start_char, "end": ent.end_char,
"label": ent.label_} for ent in self.ents]
if self.is_sentenced:
sents = list(self.sents)
data["sents"] = [{"start": sent.start_char, "end": sent.end_char}
for sent in sents]
if self.cats:
data["cats"] = self.cats
data["tokens"] = []
for token in self:
token_data = {"id": token.i, "start": token.idx, "end": token.idx + len(token)}
if self.is_tagged:
token_data["pos"] = token.pos_
token_data["tag"] = token.tag_
if self.is_parsed:
token_data["dep"] = token.dep_
token_data["head"] = token.head.i
data["tokens"].append(token_data)
if underscore:
data["_"] = {}
for attr in underscore:
if not self.has_extension(attr):
raise ValueError(Errors.E106.format(attr=attr, opts=underscore))
value = self._.get(attr)
if not srsly.is_json_serializable(value):
raise ValueError(Errors.E107.format(attr=attr, value=repr(value)))
data["_"][attr] = value
return data
def to_utf8_array(self, int nr_char=-1):
"""Encode word strings to utf8, and export to a fixed-width array
of characters. Characters are placed into the array in the order:
0, -1, 1, -2, etc
For example, if the array is sliced array[:, :8], the array will
contain the first 4 characters and last 4 characters of each word ---
with the middle characters clipped out. The value 255 is used as a pad
value.
"""
byte_strings = [token.orth_.encode('utf8') for token in self]
if nr_char == -1:
nr_char = max(len(bs) for bs in byte_strings)
cdef np.ndarray output = numpy.zeros((len(byte_strings), nr_char), dtype='uint8')
output.fill(255)
cdef int i, j, start_idx, end_idx
cdef bytes byte_string
cdef unsigned char utf8_char
for i, byte_string in enumerate(byte_strings):
j = 0
start_idx = 0
end_idx = len(byte_string) - 1
while j < nr_char and start_idx <= end_idx:
output[i, j] = <unsigned char>byte_string[start_idx]
start_idx += 1
j += 1
if j < nr_char and start_idx <= end_idx:
output[i, j] = <unsigned char>byte_string[end_idx]
end_idx -= 1
j += 1
return output
cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2:
cdef int i
for i in range(length):
if tokens[i].idx == start_char:
return i
else:
return -1
cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2:
cdef int i
for i in range(length):
if tokens[i].idx + tokens[i].lex.length == end_char:
return i
else:
return -1
cdef int set_children_from_heads(TokenC* tokens, int length) except -1:
cdef TokenC* head
cdef TokenC* child
cdef int i
# Set number of left/right children to 0. We'll increment it in the loops.
for i in range(length):
tokens[i].l_kids = 0
tokens[i].r_kids = 0
tokens[i].l_edge = i
tokens[i].r_edge = i
cdef int loop_count = 0
cdef bint heads_within_sents = False
# Try up to 10 iterations of adjusting lr_kids and lr_edges in order to
# handle non-projective dependency parses, stopping when all heads are
# within their respective sentence boundaries. We have documented cases
# that need at least 4 iterations, so this is to be on the safe side
# without risking getting stuck in an infinite loop if something is
# terribly malformed.
while not heads_within_sents:
heads_within_sents = _set_lr_kids_and_edges(tokens, length, loop_count)
if loop_count > 10:
warnings.warn(Warnings.W026)
break
loop_count += 1
# Set sentence starts
for i in range(length):
if tokens[i].head == 0 and tokens[i].dep != 0:
tokens[tokens[i].l_edge].sent_start = True
cdef int _set_lr_kids_and_edges(TokenC* tokens, int length, int loop_count) except -1:
# May be called multiple times due to non-projectivity. See issues #3170
# and #4688.
# Set left edges
cdef TokenC* head
cdef TokenC* child
cdef int i, j
for i in range(length):
child = &tokens[i]
head = &tokens[i + child.head]
if child < head and loop_count == 0:
head.l_kids += 1
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
# Set right edges - same as above, but iterate in reverse
for i in range(length-1, -1, -1):
child = &tokens[i]
head = &tokens[i + child.head]
if child > head and loop_count == 0:
head.r_kids += 1
if child.r_edge > head.r_edge:
head.r_edge = child.r_edge
if child.l_edge < head.l_edge:
head.l_edge = child.l_edge
# Get sentence start positions according to current state
sent_starts = set()
for i in range(length):
if tokens[i].head == 0 and tokens[i].dep != 0:
sent_starts.add(tokens[i].l_edge)
cdef int curr_sent_start = 0
cdef int curr_sent_end = 0
# Check whether any heads are not within the current sentence
for i in range(length):
if (i > 0 and i in sent_starts) or i == length - 1:
curr_sent_end = i
for j in range(curr_sent_start, curr_sent_end):
if tokens[j].head + j < curr_sent_start or tokens[j].head + j >= curr_sent_end + 1:
return False
curr_sent_start = i
return True
cdef int _get_tokens_lca(Token token_j, Token token_k):
"""Given two tokens, returns the index of the lowest common ancestor
(LCA) among the two. If they have no common ancestor, -1 is returned.
token_j (Token): a token.
token_k (Token): another token.
RETURNS (int): index of lowest common ancestor, or -1 if the tokens
have no common ancestor.
"""
if token_j == token_k:
return token_j.i
elif token_j.head == token_k:
return token_k.i
elif token_k.head == token_j:
return token_j.i
token_j_ancestors = set(token_j.ancestors)
if token_k in token_j_ancestors:
return token_k.i
for token_k_ancestor in token_k.ancestors:
if token_k_ancestor == token_j:
return token_j.i
if token_k_ancestor in token_j_ancestors:
return token_k_ancestor.i
return -1
cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end):
"""Given a doc and a start and end position defining a set of contiguous
tokens within it, returns a matrix of Lowest Common Ancestors (LCA), where
LCA[i, j] is the index of the lowest common ancestor among token i and j.
If the tokens have no common ancestor within the specified span,
LCA[i, j] will be -1.
doc (Doc): The index of the token, or the slice of the document
start (int): First token to be included in the LCA matrix.
end (int): Position of next to last token included in the LCA matrix.
RETURNS (int [:, :]): memoryview of numpy.array[ndim=2, dtype=numpy.int32],
with shape (n, n), where n = len(doc).
"""
cdef int [:,:] lca_matrix
n_tokens= end - start
lca_mat = numpy.empty((n_tokens, n_tokens), dtype=numpy.int32)
lca_mat.fill(-1)
lca_matrix = lca_mat
for j in range(n_tokens):
token_j = doc[start + j]
# the common ancestor of token and itself is itself:
lca_matrix[j, j] = j
# we will only iterate through tokens in the same sentence
sent = token_j.sent
sent_start = sent.start
j_idx_in_sent = start + j - sent_start
n_missing_tokens_in_sent = len(sent) - j_idx_in_sent
# make sure we do not go past `end`, in cases where `end` < sent.end
max_range = min(j + n_missing_tokens_in_sent, end)
for k in range(j + 1, max_range):
lca = _get_tokens_lca(token_j, doc[start + k])
# if lca is outside of span, we set it to -1
if not start <= lca < end:
lca_matrix[j, k] = -1
lca_matrix[k, j] = -1
else:
lca_matrix[j, k] = lca - start
lca_matrix[k, j] = lca - start
return lca_matrix
def pickle_doc(doc):
bytes_data = doc.to_bytes(exclude=["vocab", "user_data"])
hooks_and_data = (doc.user_data, doc.user_hooks, doc.user_span_hooks,
doc.user_token_hooks)
return (unpickle_doc, (doc.vocab, srsly.pickle_dumps(hooks_and_data), bytes_data))
def unpickle_doc(vocab, hooks_and_data, bytes_data):
user_data, doc_hooks, span_hooks, token_hooks = srsly.pickle_loads(hooks_and_data)
doc = Doc(vocab, user_data=user_data).from_bytes(bytes_data, exclude=["user_data"])
doc.user_hooks.update(doc_hooks)
doc.user_span_hooks.update(span_hooks)
doc.user_token_hooks.update(token_hooks)
return doc
copy_reg.pickle(Doc, pickle_doc, unpickle_doc)
def remove_label_if_necessary(attributes):
# More deprecated attribute handling =/
if "label" in attributes:
attributes["ent_type"] = attributes.pop("label")
def fix_attributes(doc, attributes):
if "label" in attributes and "ent_type" not in attributes:
if isinstance(attributes["label"], int):
attributes[ENT_TYPE] = attributes["label"]
else:
attributes[ENT_TYPE] = doc.vocab.strings[attributes["label"]]
if "ent_type" in attributes:
attributes[ENT_TYPE] = attributes["ent_type"]
def get_entity_info(ent_info):
if isinstance(ent_info, Span):
ent_type = ent_info.label
ent_kb_id = ent_info.kb_id
start = ent_info.start
end = ent_info.end
elif len(ent_info) == 3:
ent_type, start, end = ent_info
ent_kb_id = 0
elif len(ent_info) == 4:
ent_type, ent_kb_id, start, end = ent_info
else:
ent_id, ent_kb_id, ent_type, start, end = ent_info
return ent_type, ent_kb_id, start, end