def __call__(self, doc):
text = urllib.parse.quote_plus(doc.text)
result = urllib.request.urlopen(self.url + text).read()
annotationset = ET.fromstring(result).find('AnnotationSet')
for annotation in annotationset.getchildren():
if annotation.get('Type') != 'Token':
continue
word_index = int(annotation.get('StartNode'))
token = self.get_token_by_idx(word_index, doc)
ner_tag = self.get_value_from_annotation(annotation, 'NER-BIO1')
ner_tag = ner_tag.strip()
if ner_tag != 'O': # this is a capital o letter
ner_tag_parts = ner_tag.split('-')
label = doc.vocab.strings[ner_tag_parts[1]]
if ner_tag_parts[0] == '1':
doc.ents = (list(doc.ents) +
[Span(doc, token.i, token.i + 1, label=label)])
elif ner_tag_parts[0] == 'B':
self.ner_begin_idx = token.i
elif ner_tag_parts[0] == 'E':
if self.ner_begin_idx is None:
raise Exception('Found end of ner,'
'when looking for beginning')
else:
span = Span(doc,
self.ner_begin_idx,
token.i + 1,
label=label)
span.merge()
doc.ents = (list(doc.ents) + [span])
self.ner_begin_idx = None
return doc
def __call__(self, doc):
entities = []
for c in self.pipe_:
doc = c(doc, entities)
# add extracted entities to doc.ents
for e in entities:
start, end, label = e
span = Span(doc, start, end, label=label)
doc.ents = list(doc.ents) + [span]
# merge entities into one token? (default to False)
if (self.merge_entity_spans):
for span in doc.ents:
span.merge()
return doc
def init_doc(self, words, spaces, spans):
## Creating a new document with the text
doc = Doc(self.nlp.vocab, words=words, spaces=spaces)
## Loading GROBID entities in the spaCY document
entities = []
for s in spans:
span = Span(doc=doc,
start=s['tokenStart'],
end=s['tokenEnd'],
label=s['type'])
span._.set('id', str(s['id']))
if 'boundingBoxes' in s:
span._.set('bounding_boxes', s['boundingBoxes'])
if 'formattedText' in s:
span._.set('formattedText', s['formattedText'])
if 'links' in s:
span._.set('links', s['links'])
if 'linkable' in s:
span._.set('linkable', s['linkable'])
entities.append(span)
doc.ents = entities
# print("Entities: " + str(doc.ents))
for span in entities:
# Iterate over all spans and merge them into one token. This is done
# after setting the entities – otherwise, it would cause mismatched
# indices!
span.merge()
for token in span:
token._.id = span._.id
token._.bounding_boxes = span._.bounding_boxes
token._.formattedText = span._.formattedText
token._.links = span._.links
token._.linkable = span._.linkable
self.nlp.tagger(doc)
self.nlp.parser(doc)
## Merge entities and phrase nouns, but only when they are not overlapping,
# to avoid loosing the entity type information
phrases_ents = self.extract_phrases_ents(doc)
# print(phrases_ents)
for span in phrases_ents:
# print("Span " + str(span))
overlapping = False
for ent in entities:
# print(ent)
if ((span.start <= ent.start <= span.end)
or (span.start <= ent.end >= span.end)
or (span.start >= ent.start and span.end <= ent.end)
or (span.start <= ent.start and span.end >= ent.end)):
overlapping = True
break
# Entities and phrase noun are not overlapping
if not overlapping:
span.merge()
# self.nlp.tagger(doc)
# self.nlp.parser(doc)
return doc
