def extract_textual_features(
candidates: Union[Candidate, List[Candidate]],
) -> Iterator[Tuple[int, str, int]]:
"""Extract textual features.
:param candidates: A list of candidates to extract features from
"""
candidates = candidates if isinstance(candidates, list) else [candidates]
for candidate in candidates:
args = tuple([m.context for m in candidate.get_mentions()])
if not (isinstance(args[0], (SpanMention, ImplicitSpanMention))):
raise ValueError(
f"Accepts Span/ImplicitSpan-type mentions, {type(args[0])}-type found."
)
# Unary candidates
if len(args) == 1:
span: Union[SpanMention, ImplicitSpanMention] = args[0]
if span.sentence.is_lingual():
get_tdl_feats = _compile_entity_feature_generator()
xmltree = corenlp_to_xmltree(span.sentence)
sidxs = list(
range(span.get_word_start_index(),
span.get_word_end_index() + 1))
if len(sidxs) > 0:
# Add DDLIB entity features
for f in _get_ddlib_feats(span, get_as_dict(span.sentence),
sidxs):
yield candidate.id, f"DDL_{f}", DEF_VALUE
# Add TreeDLib entity features
if span.stable_id not in unary_tdl_feats:
unary_tdl_feats[span.stable_id] = set()
for f in get_tdl_feats(xmltree.root, sidxs):
unary_tdl_feats[span.stable_id].add(f)
for f in unary_tdl_feats[span.stable_id]:
yield candidate.id, f"TDL_{f}", DEF_VALUE
for f in _get_word_feats(span):
yield candidate.id, f"BASIC_{f}", DEF_VALUE
# Multinary candidates
else:
spans = args
if all([span.sentence.is_lingual() for span in spans]):
get_tdl_feats = compile_relation_feature_generator(
is_multary=True)
sents = [get_as_dict(span.sentence) for span in spans]
xmltree = corenlp_to_xmltree(spans[0].sentence)
s_idxs = [
list(
range(span.get_word_start_index(),
span.get_word_end_index() + 1)) for span in spans
]
if all([len(s_idx) > 0 for s_idx in s_idxs]):
# Add DDLIB entity features for relation
for span, sent, s_idx, i in zip(spans, sents, s_idxs,
range(len(spans))):
for f in _get_ddlib_feats(span, sent, s_idx):
yield candidate.id, f"DDL_e{i}_{f}", DEF_VALUE
# Add TreeDLib relation features
if candidate.id not in multinary_tdl_feats:
multinary_tdl_feats[candidate.id] = set()
for f in get_tdl_feats(xmltree.root, s_idxs):
multinary_tdl_feats[candidate.id].add(f)
for f in multinary_tdl_feats[candidate.id]:
yield candidate.id, f"TDL_{f}", DEF_VALUE
for i, span in enumerate(spans):
for f in _get_word_feats(span):
yield candidate.id, f"BASIC_e{i}_{f}", DEF_VALUE
def get_content_feats(candidates):
candidates = candidates if isinstance(candidates, list) else [candidates]
for candidate in candidates:
args = tuple([m.context for m in candidate.get_mentions()])
if not (isinstance(args[0], TemporarySpanMention)):
raise ValueError(
f"Accepts Span-type arguments, {type(candidate)}-type found.")
# Unary candidates
if len(args) == 1:
span = args[0]
if span.sentence.is_lingual():
get_tdl_feats = compile_entity_feature_generator()
sent = get_as_dict(span.sentence)
xmltree = corenlp_to_xmltree(sent)
sidxs = list(
range(span.get_word_start_index(),
span.get_word_end_index() + 1))
if len(sidxs) > 0:
# Add DDLIB entity features
for f in get_ddlib_feats(span, sent, sidxs):
yield candidate.id, f"DDL_{f}", DEF_VALUE
# Add TreeDLib entity features
if span.stable_id not in unary_tdl_feats:
unary_tdl_feats[span.stable_id] = set()
for f in get_tdl_feats(xmltree.root, sidxs):
unary_tdl_feats[span.stable_id].add(f)
for f in unary_tdl_feats[span.stable_id]:
yield candidate.id, f"TDL_{f}", DEF_VALUE
else:
for f in get_word_feats(span):
yield candidate.id, f"BASIC_{f}", DEF_VALUE
# Binary candidates
elif len(args) == 2:
span1, span2 = args
if span1.sentence.is_lingual() and span2.sentence.is_lingual():
get_tdl_feats = compile_relation_feature_generator()
sent1 = get_as_dict(span1.sentence)
sent2 = get_as_dict(span2.sentence)
xmltree = corenlp_to_xmltree(get_as_dict(span1.sentence))
s1_idxs = list(
range(span1.get_word_start_index(),
span1.get_word_end_index() + 1))
s2_idxs = list(
range(span2.get_word_start_index(),
span2.get_word_end_index() + 1))
if len(s1_idxs) > 0 and len(s2_idxs) > 0:
# Add DDLIB entity features for relation
for f in get_ddlib_feats(span1, sent1, s1_idxs):
yield candidate.id, f"DDL_e1_{f}", DEF_VALUE
for f in get_ddlib_feats(span2, sent2, s2_idxs):
yield candidate.id, f"DDL_e2_{f}", DEF_VALUE
# Add TreeDLib relation features
if candidate.id not in binary_tdl_feats:
binary_tdl_feats[candidate.id] = set()
for f in get_tdl_feats(xmltree.root, s1_idxs, s2_idxs):
binary_tdl_feats[candidate.id].add(f)
for f in binary_tdl_feats[candidate.id]:
yield candidate.id, f"TDL_{f}", DEF_VALUE
else:
for f in get_word_feats(span1):
yield candidate.id, f"BASIC_e1_{f}", DEF_VALUE
for f in get_word_feats(span2):
yield candidate.id, f"BASIC_e2_{f}", DEF_VALUE
else:
raise NotImplementedError(
"Only handles unary and binary candidates currently")
def extract_textual_features(
candidates: Union[Candidate, List[Candidate]],
) -> Iterator[Tuple[int, str, int]]:
"""Extract textual features.
:param candidates: A list of candidates to extract features from
:type candidates: list
"""
candidates = candidates if isinstance(candidates, list) else [candidates]
for candidate in candidates:
args = tuple([m.context for m in candidate.get_mentions()])
if not (isinstance(args[0], (SpanMention, ImplicitSpanMention))):
raise ValueError(
f"Accepts Span/ImplicitSpan-type mentions, {type(args[0])}-type found."
)
# Unary candidates
if len(args) == 1:
span: Union[SpanMention, ImplicitSpanMention] = args[0]
if span.sentence.is_lingual():
get_tdl_feats = _compile_entity_feature_generator()
xmltree = corenlp_to_xmltree(span.sentence)
sidxs = list(
range(span.get_word_start_index(), span.get_word_end_index() + 1)
)
if len(sidxs) > 0:
# Add DDLIB entity features
for f in _get_ddlib_feats(span, get_as_dict(span.sentence), sidxs):
yield candidate.id, f"DDL_{f}", DEF_VALUE
# Add TreeDLib entity features
if span.stable_id not in unary_tdl_feats:
unary_tdl_feats[span.stable_id] = set()
for f in get_tdl_feats(xmltree.root, sidxs):
unary_tdl_feats[span.stable_id].add(f)
for f in unary_tdl_feats[span.stable_id]:
yield candidate.id, f"TDL_{f}", DEF_VALUE
for f in _get_word_feats(span):
yield candidate.id, f"BASIC_{f}", DEF_VALUE
# Binary candidates
elif len(args) == 2:
span1, span2 = args
if span1.sentence.is_lingual() and span2.sentence.is_lingual():
get_tdl_feats = compile_relation_feature_generator()
sent1 = get_as_dict(span1.sentence)
sent2 = get_as_dict(span2.sentence)
xmltree = corenlp_to_xmltree(span1.sentence)
s1_idxs = list(
range(span1.get_word_start_index(), span1.get_word_end_index() + 1)
)
s2_idxs = list(
range(span2.get_word_start_index(), span2.get_word_end_index() + 1)
)
if len(s1_idxs) > 0 and len(s2_idxs) > 0:
# Add DDLIB entity features for relation
for f in _get_ddlib_feats(span1, sent1, s1_idxs):
yield candidate.id, f"DDL_e1_{f}", DEF_VALUE
for f in _get_ddlib_feats(span2, sent2, s2_idxs):
yield candidate.id, f"DDL_e2_{f}", DEF_VALUE
# Add TreeDLib relation features
if candidate.id not in binary_tdl_feats:
binary_tdl_feats[candidate.id] = set()
for f in get_tdl_feats(xmltree.root, s1_idxs, s2_idxs):
binary_tdl_feats[candidate.id].add(f)
for f in binary_tdl_feats[candidate.id]:
yield candidate.id, f"TDL_{f}", DEF_VALUE
for f in _get_word_feats(span1):
yield candidate.id, f"BASIC_e1_{f}", DEF_VALUE
for f in _get_word_feats(span2):
yield candidate.id, f"BASIC_e2_{f}", DEF_VALUE
else:
raise NotImplementedError(
"Only handles unary and binary candidates currently"
)
