def _get_seq_features(sentence, span):
"""Yield the sequence features in a Span
These include:
- words sequence in the span
- lemmas sequence in the span
- NER tags sequence in the span
- POS tags sequence in the span
Args:
sentence: a list of Word objects
span: the Span
"""
word_seq_feat = "WORD_SEQ_[" + " ".join(materialize_span(
sentence, span, lambda x: x.word)) + "]"
yield word_seq_feat
lemma_seq_feat = "LEMMA_SEQ_[" + " ".join(materialize_span(
sentence, span, lambda x: x.word)) + "]"
yield lemma_seq_feat
ner_seq_feat = "NER_SEQ_[" + " ".join(materialize_span(
sentence, span, lambda x: x.ner)) + "]"
yield ner_seq_feat
pos_seq_feat = "POS_SEQ_[" + " ".join(materialize_span(
sentence, span, lambda x: x.pos)) + "]"
yield pos_seq_feat
def _get_seq_features(sentence, span):
"""Yield the sequence features in a Span
These include:
- words sequence in the span
- lemmas sequence in the span
- NER tags sequence in the span
- POS tags sequence in the span
Args:
sentence: a list of Word objects
span: the Span
"""
word_seq_feat = "WORD_SEQ_[" + " ".join(
materialize_span(sentence, span, lambda x: x.word)) + "]"
yield word_seq_feat
lemma_seq_feat = "LEMMA_SEQ_[" + " ".join(
materialize_span(sentence, span, lambda x: x.word)) + "]"
yield lemma_seq_feat
ner_seq_feat = "NER_SEQ_[" + " ".join(
materialize_span(sentence, span, lambda x: x.ner)) + "]"
yield ner_seq_feat
pos_seq_feat = "POS_SEQ_[" + " ".join(
materialize_span(sentence, span, lambda x: x.pos)) + "]"
yield pos_seq_feat
def get_generic_features_mention(sentence, span, length_bin_size=5):
"""Yield 'generic' features for a mention in a sentence.
Args:
sentence: a list of Word objects
span: a Span namedtuple
length_bin_size: the size of the bins for the length feature
"""
# Mention sequence features (words, lemmas, ners, and poses)
for seq_feat in _get_seq_features(sentence, span):
yield seq_feat
# Window (left and right, up to size 3, with combinations) around the
# mention
for window_feat in _get_window_features(sentence, span):
yield window_feat
# Is (substring of) mention in a dictionary?
for dict_indicator_feat in _get_dictionary_indicator_features(
sentence, span):
yield dict_indicator_feat
# Dependency path(s) from mention to keyword(s). Various transformations of
# the dependency path are done.
for (i, j) in _get_substring_indices(len(sentence), MAX_KW_LENGTH):
if i >= span.begin_word_id and i < span.begin_word_id + span.length:
continue
if j > span.begin_word_id and j < span.begin_word_id + span.length:
continue
is_in_dictionary = False
for dict_id in dictionaries:
if " ".join(map(lambda x: x.word, sentence[i:j])) in \
dictionaries[dict_id]:
is_in_dictionary = True
yield "KW_IND_[" + dict_id + "]"
break
if is_in_dictionary:
kw_span = Span(begin_word_id=i, length=j-i)
for dep_path_feature in _get_min_dep_path_features(
sentence, span, kw_span, "KW"):
yield dep_path_feature
# The mention starts with a capital
if sentence[span.begin_word_id].word[0].isupper():
yield "STARTS_WITH_CAPITAL"
# Length of the mention
length = len(" ".join(materialize_span(sentence, span, lambda x: x.word)))
bin_id = length // length_bin_size
length_feat = "LENGTH_" + str(bin_id)
yield length_feat
def get_generic_features_mention(sentence, span, length_bin_size=5):
"""Yield 'generic' features for a mention in a sentence.
Args:
sentence: a list of Word objects
span: a Span namedtuple
length_bin_size: the size of the bins for the length feature
"""
# Mention sequence features (words, lemmas, ners, and poses)
for seq_feat in _get_seq_features(sentence, span):
yield seq_feat
# Window (left and right, up to size 3, with combinations) around the
# mention
for window_feat in _get_window_features(sentence, span):
yield window_feat
# Is (substring of) mention in a dictionary?
for dict_indicator_feat in _get_dictionary_indicator_features(
sentence, span):
yield dict_indicator_feat
# Dependency path(s) from mention to keyword(s). Various transformations of
# the dependency path are done.
for (i, j) in _get_substring_indices(len(sentence), MAX_KW_LENGTH):
if i >= span.begin_word_id and i < span.begin_word_id + span.length:
continue
if j > span.begin_word_id and j < span.begin_word_id + span.length:
continue
is_in_dictionary = False
for dict_id in dictionaries:
if " ".join(map(lambda x: x.word, sentence[i:j])) in \
dictionaries[dict_id]:
is_in_dictionary = True
yield "KW_IND_[" + dict_id + "]"
break
if is_in_dictionary:
kw_span = Span(begin_word_id=i, length=j - i)
for dep_path_feature in _get_min_dep_path_features(
sentence, span, kw_span, "KW"):
yield dep_path_feature
# The mention starts with a capital
if sentence[span.begin_word_id].word[0].isupper():
yield "STARTS_WITH_CAPITAL"
# Length of the mention
length = len(" ".join(materialize_span(sentence, span, lambda x: x.word)))
bin_id = length // length_bin_size
length_feat = "LENGTH_" + str(bin_id)
yield length_feat
def get_generic_features_relation(sentence, span1, span2, length_bin_size=5):
"""Yield 'generic' features for a relation in a sentence.
Args:
sentence: a list of Word objects
span1: the first Span of the relation
span2: the second Span of the relation
length_bin_size: the size of the bins for the length feature
"""
# Check whether the order of the spans is inverted. We use this information
# to add a prefix to *all* the features.
order = sorted([
span1.begin_word_id, span1.begin_word_id + span1.length,
span2.begin_word_id, span2.begin_word_id + span2.length])
begin = order[0]
betw_begin = order[1]
betw_end = order[2]
end = order[3]
if begin == span2.begin_word_id:
inverted = "INV_"
yield "IS_INVERTED"
else:
inverted = ""
betw_span = Span(begin_word_id=betw_begin, length=betw_end - betw_begin)
covering_span = Span(begin_word_id=begin, length=end - begin)
# Words, Lemmas, Ners, and Poses sequence between the mentions
for seq_feat in _get_seq_features(sentence, betw_span):
yield inverted + seq_feat
# Window feature (left and right, up to size 3, combined)
for window_feat in _get_window_features(
sentence, covering_span, isolated=False):
yield inverted + window_feat
# Ngrams of up to size 3 between the mentions
for ngram_feat in _get_ngram_features(sentence, betw_span):
yield inverted + ngram_feat
# Indicator features of whether the mentions are in dictionaries
found1 = False
for feat1 in _get_dictionary_indicator_features(
sentence, span1, prefix=inverted + "IN_DICT"):
found1 = True
found2 = False
for feat2 in _get_dictionary_indicator_features(
sentence, span2, prefix=""):
found2 = True
yield feat1 + feat2
if not found2:
yield feat1 + "_[_NONE]"
if not found1:
for feat2 in _get_dictionary_indicator_features(
sentence, span2, prefix=""):
found2 = True
yield inverted + "IN_DICT_[_NONE]" + feat2
# Dependency path (and transformations) between the mention
for betw_dep_path_feature in _get_min_dep_path_features(
sentence, span1, span2, inverted + "BETW"):
yield betw_dep_path_feature
# Dependency paths (and transformations) between the mentions and keywords
for (i, j) in _get_substring_indices(len(sentence), MAX_KW_LENGTH):
if (i >= begin and i < betw_begin) or (i >= betw_end and i < end):
continue
if (j > begin and j <= betw_begin) or (j > betw_end and j <= end):
continue
is_in_dictionary = False
for dict_id in dictionaries:
if " ".join(map(lambda x: x.word, sentence[i:j])) in \
dictionaries[dict_id]:
is_in_dictionary = True
yield inverted + "KW_IND_[" + dict_id + "]"
break
if is_in_dictionary:
kw_span = Span(begin_word_id=i, length=j-i)
path1 = _get_min_dep_path(sentence, span1, kw_span)
lemmas1 = []
labels1 = []
for edge in path1:
lemmas1.append(edge.word2.word)
labels1.append(edge.label)
both1 = []
for j in range(len(labels1)):
both1.append(labels1[j])
both1.append(lemmas1[j])
both1 = both1[:-1]
path2 = _get_min_dep_path(sentence, span2, kw_span)
lemmas2 = []
labels2 = []
for edge in path2:
lemmas2.append(edge.word2.word)
labels2.append(edge.label)
both2 = []
for j in range(len(labels2)):
both2.append(labels2[j])
both2.append(lemmas2[j])
both2 = both2[:-1]
yield inverted + "KW_[" + " ".join(both1) + "]_[" + \
" ".join(both2) + "]"
yield inverted + "KW_L_[" + " ".join(labels1) + "]_[" + \
" ".join(labels2) + "]"
for j in range(1, len(both1), 2):
for dict_id in dictionaries:
if both1[j] in dictionaries[dict_id]:
both1[j] = "DICT_" + str(dict_id)
break # Picking up the first dictionary we find
for j in range(1, len(both2), 2):
for dict_id in dictionaries:
if both2[j] in dictionaries[dict_id]:
both2[j] = "DICT_" + str(dict_id)
break # Picking up the first dictionary we find
yield inverted + "KW_D_[" + " ".join(both1) + "]_[" + \
" ".join(both2) + "]"
# The lengths of the mentions
first_length = len(" ".join(materialize_span(
sentence, span1, lambda x: x.word)))
second_length = len(" ".join(materialize_span(
sentence, span2, lambda x: x.word)))
first_bin_id = first_length // length_bin_size
second_bin_id = second_length // length_bin_size
length_feat = inverted + "LENGTHS_[" + str(first_bin_id) + "_" + \
str(second_bin_id) + "]"
yield length_feat
def get_generic_features_relation(sentence, span1, span2, length_bin_size=5):
"""Yield 'generic' features for a relation in a sentence.
Args:
sentence: a list of Word objects
span1: the first Span of the relation
span2: the second Span of the relation
length_bin_size: the size of the bins for the length feature
"""
# Check whether the order of the spans is inverted. We use this information
# to add a prefix to *all* the features.
order = sorted([
span1.begin_word_id, span1.begin_word_id + span1.length,
span2.begin_word_id, span2.begin_word_id + span2.length
])
begin = order[0]
betw_begin = order[1]
betw_end = order[2]
end = order[3]
if begin == span2.begin_word_id:
inverted = "INV_"
yield "IS_INVERTED"
else:
inverted = ""
betw_span = Span(begin_word_id=betw_begin, length=betw_end - betw_begin)
covering_span = Span(begin_word_id=begin, length=end - begin)
# Words, Lemmas, Ners, and Poses sequence between the mentions
for seq_feat in _get_seq_features(sentence, betw_span):
yield inverted + seq_feat
# Window feature (left and right, up to size 3, combined)
for window_feat in _get_window_features(sentence,
covering_span,
isolated=False):
yield inverted + window_feat
# Ngrams of up to size 3 between the mentions
for ngram_feat in _get_ngram_features(sentence, betw_span):
yield inverted + ngram_feat
# Indicator features of whether the mentions are in dictionaries
found1 = False
for feat1 in _get_dictionary_indicator_features(sentence,
span1,
prefix=inverted +
"IN_DICT"):
found1 = True
found2 = False
for feat2 in _get_dictionary_indicator_features(sentence,
span2,
prefix=""):
found2 = True
yield feat1 + feat2
if not found2:
yield feat1 + "_[_NONE]"
if not found1:
for feat2 in _get_dictionary_indicator_features(sentence,
span2,
prefix=""):
found2 = True
yield inverted + "IN_DICT_[_NONE]" + feat2
# Dependency path (and transformations) between the mention
for betw_dep_path_feature in _get_min_dep_path_features(
sentence, span1, span2, inverted + "BETW"):
yield betw_dep_path_feature
# Dependency paths (and transformations) between the mentions and keywords
for (i, j) in _get_substring_indices(len(sentence), MAX_KW_LENGTH):
if (i >= begin and i < betw_begin) or (i >= betw_end and i < end):
continue
if (j > begin and j <= betw_begin) or (j > betw_end and j <= end):
continue
is_in_dictionary = False
for dict_id in dictionaries:
if " ".join(map(lambda x: x.word, sentence[i:j])) in \
dictionaries[dict_id]:
is_in_dictionary = True
yield inverted + "KW_IND_[" + dict_id + "]"
break
if is_in_dictionary:
kw_span = Span(begin_word_id=i, length=j - i)
path1 = _get_min_dep_path(sentence, span1, kw_span)
lemmas1 = []
labels1 = []
for edge in path1:
lemmas1.append(edge.word2.word)
labels1.append(edge.label)
both1 = []
for j in range(len(labels1)):
both1.append(labels1[j])
both1.append(lemmas1[j])
both1 = both1[:-1]
path2 = _get_min_dep_path(sentence, span2, kw_span)
lemmas2 = []
labels2 = []
for edge in path2:
lemmas2.append(edge.word2.word)
labels2.append(edge.label)
both2 = []
for j in range(len(labels2)):
both2.append(labels2[j])
both2.append(lemmas2[j])
both2 = both2[:-1]
yield inverted + "KW_[" + " ".join(both1) + "]_[" + \
" ".join(both2) + "]"
yield inverted + "KW_L_[" + " ".join(labels1) + "]_[" + \
" ".join(labels2) + "]"
for j in range(1, len(both1), 2):
for dict_id in dictionaries:
if both1[j] in dictionaries[dict_id]:
both1[j] = "DICT_" + str(dict_id)
break # Picking up the first dictionary we find
for j in range(1, len(both2), 2):
for dict_id in dictionaries:
if both2[j] in dictionaries[dict_id]:
both2[j] = "DICT_" + str(dict_id)
break # Picking up the first dictionary we find
yield inverted + "KW_D_[" + " ".join(both1) + "]_[" + \
" ".join(both2) + "]"
# The lengths of the mentions
first_length = len(" ".join(
materialize_span(sentence, span1, lambda x: x.word)))
second_length = len(" ".join(
materialize_span(sentence, span2, lambda x: x.word)))
first_bin_id = first_length // length_bin_size
second_bin_id = second_length // length_bin_size
length_feat = inverted + "LENGTHS_[" + str(first_bin_id) + "_" + \
str(second_bin_id) + "]"
yield length_feat