def annotated_text(
self,
text: str,
annotations: Collection[AnnotatedSpan],
*,
text_offsets: Optional[Span] = None,
) -> str:
"""
Mark annotations on text in an HTML-like style.
Each annotation will becomes an HTML tag wrapping the text at the corresponding offsets.
Any attributes will become HTML attributes.
This does not add any other HTML annotations (`head`, `body`, etc.), so if desired the
user should add them afterwards.
If `text_offsets` is specified, the annotations are assumed to have offsets with respect
to some larger string, where `text` is a substring of that string with offsets
`text_offsets` relative to it. You might use this, for example, to render a single
paragraph from a document.
"""
if not text_offsets:
text_offsets = Span.from_inclusive_to_exclusive(0, len(text))
check_arg(
len(text_offsets) == len(text),
f"Text offsets length {len(text_offsets)} "
f"does not match text length {len(text)}",
)
# we process the annotations to (a) ensure they all fit within the requested snippet
# and (b) shift their offsets so that all offsets are relative to the text being
# formatted
processed_annotations = self._clip_to_offsets_and_shift(
annotations, text_offsets)
ret = io.StringIO()
last_uncopied_offset = 0
for tag in self._tag_sequence(processed_annotations):
if last_uncopied_offset < tag.offset:
ret.write(text[last_uncopied_offset:tag.offset])
last_uncopied_offset = tag.offset
ret.write(tag.string)
# get any trailing text after last tag
if last_uncopied_offset < text_offsets.end:
ret.write(text[last_uncopied_offset:text_offsets.end])
return ret.getvalue()
def parse_text_from_source(text_justification_lookup: TextJustificationLookup,
inf_just_pattern,
inf_just_span):
match = re.search(inf_just_pattern, inf_just_span)
if match:
# source = match.group(1)
document = match.group(2)
start = int(match.group(3))
end = int(match.group(4))
text_descriptor = TextDescriptor(doceid=document,
span=Span.from_inclusive_to_exclusive(start, end + 1),
language=None)
try:
lookup = text_justification_lookup.text_for_justification(text_descriptor, 50)
return lookup.spanning_tokens, lookup.original_text
except (RuntimeError, AttributeError):
return 'None', 'None'
else:
return 'None', 'None'
metadata_from_wordpiece = doc.metadata_for(tokens_from_wordpiece)
tokens_from_spacy = metadata_from_wordpiece[
WordPieceTokenizationAnnotator.EXISTING_TOKEN_THEORY_USED_FOR_WORDPIECE
]
map_spacy_to_wordpiece_indices = metadata_from_wordpiece[
WordPieceTokenizationAnnotator.MULTIMAP_FROM_EXISTING_TO_WORDPIECE_TOKENIZATION
]
mentions_from_apf = doc_with_lots_of_mention_algos.mentions(algorithm(ApfIngester))
mentions_from_corenlp = doc_with_lots_of_mention_algos.mentions(
algorithm(CoreNLPNameFinder)
)
mentions_from_spacy = doc_with_lots_of_mention_algos.mentions(
algorithm(SpacyAnnotator)
)
s = Span.from_inclusive_to_exclusive(2778, 2915)
print("== ACE ==")
print(
"\n".join(
str(mention)
for mention in get_items_overlapping_with_this_one(s, mentions_from_apf)
)
)
print("== CoreNLP ==")
print(
"\n".join(
str(mention)
for mention in get_items_overlapping_with_this_one(s, mentions_from_corenlp)
)
)
print("== spaCy ==")
