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Add support for text-classification labels to GoldParse

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This commit is contained in:
Matthew Honnibal 2017-07-20 00:17:47 +02:00
parent 727481377e
commit 7ea50182a5
2 changed files with 20 additions and 1 deletions
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1
spacy/gold.pxd
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@ -29,6 +29,7 @@ cdef class GoldParse:
cdef public list ner cdef public list ner
cdef public list ents cdef public list ents
cdef public dict brackets cdef public dict brackets
cdef public object cats
cdef readonly list cand_to_gold cdef readonly list cand_to_gold
cdef readonly list gold_to_cand cdef readonly list gold_to_cand

20
spacy/gold.pyx
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@ -381,7 +381,8 @@ cdef class GoldParse:
make_projective=make_projective) make_projective=make_projective)
def __init__(self, doc, annot_tuples=None, words=None, tags=None, heads=None, def __init__(self, doc, annot_tuples=None, words=None, tags=None, heads=None,
deps=None, entities=None, make_projective=False): deps=None, entities=None, make_projective=False,
cats=tuple()):
"""Create a GoldParse. """Create a GoldParse.
doc (Doc): The document the annotations refer to. doc (Doc): The document the annotations refer to.
@ -392,6 +393,12 @@ cdef class GoldParse:
entities (iterable): A sequence of named entity annotations, either as entities (iterable): A sequence of named entity annotations, either as
BILUO tag strings, or as `(start_char, end_char, label)` tuples, BILUO tag strings, or as `(start_char, end_char, label)` tuples,
representing the entity positions. representing the entity positions.
cats (iterable): A sequence of labels for text classification. Each
label may be a string or an int, or a `(start_char, end_char, label)`
tuple, indicating that the label is applied to only part of the
document (usually a sentence). Unlike entity annotations, label
annotations can overlap, i.e. a single word can be covered by
multiple labelled spans.
RETURNS (GoldParse): The newly constructed object. RETURNS (GoldParse): The newly constructed object.
""" """
if words is None: if words is None:
@ -421,6 +428,17 @@ cdef class GoldParse:
self.c.has_dep = <int*>self.mem.alloc(len(doc), sizeof(int)) self.c.has_dep = <int*>self.mem.alloc(len(doc), sizeof(int))
self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition)) self.c.ner = <Transition*>self.mem.alloc(len(doc), sizeof(Transition))
self.cats = []
for item in cats:
if isinstance(item, int):
self.cats.append((0, len(doc.text), self.vocab.strings[item]))
elif isinstance(item, str):
self.cats.append((0, len(doc.text), item))
elif hasattr(item, '__len__') and len(item) == 3:
start_char, end_char, label = item
if isinstance(label, int):
label = self.vocab.strings[label]
self.cats.append((start_char, end_char, label))
self.words = [None] * len(doc) self.words = [None] * len(doc)
self.tags = [None] * len(doc) self.tags = [None] * len(doc)
self.heads = [None] * len(doc) self.heads = [None] * len(doc)