def _get_axis_ngrams(mention,
axis,
attrib="words",
n_min=1,
n_max=1,
spread=[0, 0],
lower=True):
span = _to_span(mention)
if not span.sentence.is_tabular():
yield None
return
for ngram in get_sentence_ngrams(span,
attrib=attrib,
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
if span.sentence.is_tabular():
for sentence in _get_aligned_sentences(span.sentence,
axis,
spread=spread):
for ngram in tokens_to_ngrams(getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
def get_cell_ngrams(mention, attrib="words", n_min=1, n_max=1, lower=True):
"""Get the ngrams that are in the Cell of the given mention, not including itself.
Note that if a candidate is passed in, all of its Mentions will be searched.
:param mention: The Mention whose Cell is being searched
:param attrib: The token attribute type (e.g. words, lemmas, poses)
:param n_min: The minimum n of the ngrams that should be returned
:param n_max: The maximum n of the ngrams that should be returned
:param lower: If True, all ngrams will be returned in lower case
:rtype: a *generator* of ngrams
"""
spans = _to_spans(mention)
for span in spans:
for ngram in get_sentence_ngrams(span,
attrib=attrib,
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
if span.sentence.is_tabular():
for ngram in chain.from_iterable([
tokens_to_ngrams(getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower) for sentence in
_get_table_cells(span.sentence.table)[span.sentence.cell]
if sentence != span.sentence
]):
yield ngram
def get_word_feats(span):
attrib = "words"
if span.stable_id not in unary_word_feats:
unary_word_feats[span.stable_id] = set()
for ngram in tokens_to_ngrams(span.get_attrib_tokens(attrib),
n_min=1,
n_max=2):
feature = f"CONTAINS_{attrib.upper()}_[{ngram}]"
unary_word_feats.add(feature)
for ngram in get_left_ngrams(
span,
window=settings["featurization"]["content"]["word_feature"]
["window"],
n_max=2,
attrib=attrib,
):
feature = f"LEFT_{attrib.upper()}_[{ngram}]"
unary_word_feats.add(feature)
for ngram in get_right_ngrams(
span,
window=settings["featurization"]["content"]["word_feature"]
["window"],
n_max=2,
attrib=attrib,
):
feature = f"RIGHT_{attrib.upper()}_[{ngram}]"
unary_word_feats.add(feature)
for f in unary_word_feats[span.stable_id]:
yield f
def get_neighbor_sentence_ngrams(mention,
d=1,
attrib="words",
n_min=1,
n_max=1,
lower=True):
"""Get the ngrams that are in the neighoring Sentences of the given Mention.
Note that if a candidate is passed in, all of its Mentions will be searched.
:param mention: The Mention whose neighbor Sentences are being searched
:param attrib: The token attribute type (e.g. words, lemmas, poses)
:param n_min: The minimum n of the ngrams that should be returned
:param n_max: The maximum n of the ngrams that should be returned
:param lower: If True, all ngrams will be returned in lower case
:rtype: a *generator* of ngrams
"""
spans = _to_spans(mention)
for span in spans:
for ngram in chain.from_iterable([
tokens_to_ngrams(getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower)
for sentence in span.sentence.document.sentences
if abs(sentence.position - span.sentence.position) <= d
and sentence != span.sentence
]):
yield ngram
def _get_axis_ngrams(
mention: Union[Candidate, Mention, TemporarySpanMention],
axis: str,
attrib: str = "words",
n_min: int = 1,
n_max: int = 1,
spread: List[int] = [0, 0],
lower: bool = True,
) -> Iterator[str]:
span = _to_span(mention)
if not span.sentence.is_tabular():
return
yield
for ngram in get_sentence_ngrams(span,
attrib=attrib,
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
for sentence in _get_aligned_sentences(span.sentence, axis, spread=spread):
for ngram in tokens_to_ngrams(getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
def get_left_ngrams(
mention: Union[Candidate, Mention, TemporarySpanMention],
window: int = 3,
attrib: str = "words",
n_min: int = 1,
n_max: int = 1,
lower: bool = True,
) -> Iterator[str]:
"""Get the ngrams within a window to the *left* from the sentence Context.
For higher-arity Candidates, defaults to the *first* argument.
:param mention: The Mention to evaluate. If a candidate is given, default
to its first Mention.
:param window: The number of tokens to the left of the first argument to
return.
:param attrib: The token attribute type (e.g. words, lemmas, poses)
:param n_min: The minimum n of the ngrams that should be returned
:param n_max: The maximum n of the ngrams that should be returned
:param lower: If True, all ngrams will be returned in lower case
"""
span = _to_span(mention)
i = span.get_word_start_index()
for ngram in tokens_to_ngrams(
getattr(span.sentence, attrib)[max(0, i - window):i],
n_min=n_min,
n_max=n_max,
lower=lower,
):
yield ngram
def get_head_ngrams(mention, axis=None, attrib="words", n_min=1, n_max=1, lower=True):
"""Get the ngrams from the cell in the head of the row or column.
More specifically, this returns the ngrams in the leftmost cell in a row and/or the
ngrams in the topmost cell in the column, depending on the axis parameter.
Note that if a candidate is passed in, all of its Mentions will be searched.
:param mention: The Mention whose head Cells are being returned
:param axis: Which axis {'row', 'col'} to search. If None, then both row
and col are searched.
:param attrib: The token attribute type (e.g. words, lemmas, poses)
:param n_min: The minimum n of the ngrams that should be returned
:param n_max: The maximum n of the ngrams that should be returned
:param lower: If True, all ngrams will be returned in lower case
:rtype: a *generator* of ngrams
"""
spans = _to_spans(mention)
axes = (axis,) if axis else ("row", "col")
for span in spans:
if span.sentence.is_tabular():
for axis in axes:
if getattr(span.sentence, _other_axis(axis) + "_start") == 0:
return
for sentence in getattr(
_get_head_cell(span.sentence.cell, axis), "sentences", []
):
for ngram in tokens_to_ngrams(
getattr(sentence, attrib), n_min=n_min, n_max=n_max, lower=lower
):
yield ngram
def _get_direction_ngrams(direction, c, attrib, n_min, n_max, lower,
from_sentence):
# TODO: this currently looks only in current table;
# precompute over the whole document/page instead
bbox_direction_aligned = (bbox_vert_aligned
if direction == "vert" else bbox_horz_aligned)
ngrams_space = Ngrams(n_max=n_max, split_tokens=[])
f = (lambda w: w.lower()) if lower else (lambda w: w)
spans = _to_spans(c)
for span in spans:
if not span.sentence.is_tabular() or not span.sentence.is_visual():
continue
for sentence in span.sentence.table.sentences:
if from_sentence:
if (bbox_direction_aligned(bbox_from_sentence(sentence),
bbox_from_span(span))
and sentence is not span.sentence):
for ngram in tokens_to_ngrams(getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
else:
for ts in ngrams_space.apply(sentence):
if bbox_direction_aligned(
bbox_from_span(ts), bbox_from_span(span)) and not (
sentence == span.sentence
and ts.get_span() in span.get_span()):
yield f(ts.get_span())
def get_right_ngrams(mention,
window=3,
attrib="words",
n_min=1,
n_max=1,
lower=True):
"""Get the ngrams within a window to the *right* from the sentence Context.
For higher-arity Candidates, defaults to the *last* argument.
:param mention: The Mention to evaluate. If a candidate is given, default
to its last Mention.
:param window: The number of tokens to the left of the first argument to
return
:param attrib: The token attribute type (e.g. words, lemmas, poses)
:param n_min: The minimum n of the ngrams that should be returned
:param n_max: The maximum n of the ngrams that should be returned
:param lower: If True, all ngrams will be returned in lower case
:rtype: a *generator* of ngrams
"""
span = _to_span(mention, idx=-1)
i = span.get_word_end()
for ngram in tokens_to_ngrams(
getattr(span.sentence, attrib)[i + 1:i + 1 + window],
n_min=n_min,
n_max=n_max,
lower=lower,
):
yield ngram
def _get_word_feats(span: SpanMention) -> Iterator[str]:
attrib = "words"
if span.stable_id not in unary_word_feats:
unary_word_feats[span.stable_id] = set()
for ngram in tokens_to_ngrams(span.get_attrib_tokens(attrib),
n_min=1,
n_max=2):
feature = f"CONTAINS_{attrib.upper()}_[{ngram}]"
unary_word_feats[span.stable_id].add(feature)
for ngram in get_left_ngrams(
span,
window=settings["featurization"]["textual"]["word_feature"]
["window"],
n_max=2,
attrib=attrib,
):
feature = f"LEFT_{attrib.upper()}_[{ngram}]"
unary_word_feats[span.stable_id].add(feature)
for ngram in get_right_ngrams(
span,
window=settings["featurization"]["textual"]["word_feature"]
["window"],
n_max=2,
attrib=attrib,
):
feature = f"RIGHT_{attrib.upper()}_[{ngram}]"
unary_word_feats[span.stable_id].add(feature)
unary_word_feats[span.stable_id].add((
f"SPAN_TYPE_["
f"{'IMPLICIT' if isinstance(span, ImplicitSpanMention) else 'EXPLICIT'}"
f"]"))
if span.get_span()[0].isupper():
unary_word_feats[span.stable_id].add("STARTS_WITH_CAPITAL")
unary_word_feats[span.stable_id].add(f"LENGTH_{span.get_num_words()}")
for f in unary_word_feats[span.stable_id]:
yield f
def get_cell_ngrams(
mention: Union[Candidate, Mention, TemporarySpanMention],
attrib: str = "words",
n_min: int = 1,
n_max: int = 1,
lower: bool = True,
) -> Iterator[str]:
"""Get the ngrams that are in the Cell of the given mention, not including itself.
Note that if a candidate is passed in, all of its Mentions will be searched.
Also note that if the mention is not tabular, nothing will be yielded.
:param mention: The Mention whose Cell is being searched
:param attrib: The token attribute type (e.g. words, lemmas, poses)
:param n_min: The minimum n of the ngrams that should be returned
:param n_max: The maximum n of the ngrams that should be returned
:param lower: If True, all ngrams will be returned in lower case
"""
spans = _to_spans(mention)
for span in spans:
if not span.sentence.is_tabular():
continue
for ngram in get_sentence_ngrams(span,
attrib=attrib,
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
for ngram in chain.from_iterable([
tokens_to_ngrams(getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower) for sentence in
_get_table_cells(span.sentence.table)[span.sentence.cell]
if sentence != span.sentence
]):
yield ngram
def _get_direction_ngrams(
direction: str,
c: Union[Candidate, Mention, TemporarySpanMention],
attrib: str,
n_min: int,
n_max: int,
lower: bool,
from_sentence: bool,
) -> Iterator[str]:
bbox_direction_aligned = (bbox_vert_aligned
if direction == "vert" else bbox_horz_aligned)
ngrams_space = Ngrams(n_max=n_max, split_tokens=[])
f = (lambda w: w.lower()) if lower else (lambda w: w)
spans = _to_spans(c)
for span in spans:
if not span.sentence.is_visual():
continue
for sentence in span.sentence.document.sentences:
# Skip if not in the same page.
if span.sentence.get_bbox().page != sentence.get_bbox().page:
continue
if from_sentence:
if (bbox_direction_aligned(sentence.get_bbox(),
span.get_bbox()) and sentence
is not span.sentence # not from its Sentence
):
for ngram in tokens_to_ngrams(getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
else:
for ts in ngrams_space.apply(sentence):
if ( # True if visually aligned AND not from itself.
bbox_direction_aligned(ts.get_bbox(),
span.get_bbox())
and ts not in span and span not in ts):
yield f(ts.get_span())
def get_neighbor_cell_ngrams(mention,
dist=1,
directions=False,
attrib="words",
n_min=1,
n_max=1,
lower=True):
"""
Get the ngrams from all Cells that are within a given Cell distance in one
direction from the given Mention.
Note that if a candidate is passed in, all of its Mentions will be
searched. If `directions=True``, each ngram will be returned with a
direction in {'UP', 'DOWN', 'LEFT', 'RIGHT'}.
:param mention: The Mention whose neighbor Cells are being searched
:param dist: The Cell distance within which a neighbor Cell must be to be
considered
:param directions: A Boolean expressing whether or not to return the
direction of each ngram
:param attrib: The token attribute type (e.g. words, lemmas, poses)
:param n_min: The minimum n of the ngrams that should be returned
:param n_max: The maximum n of the ngrams that should be returned
:param lower: If True, all ngrams will be returned in lower case
:rtype: a *generator* of ngrams (or (ngram, direction) tuples if directions=True)
"""
# TODO: Fix this to be more efficient (optimize with SQL query)
spans = _to_spans(mention)
for span in spans:
for ngram in get_sentence_ngrams(span,
attrib=attrib,
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
if span.sentence.is_tabular():
root_cell = span.sentence.cell
for sentence in chain.from_iterable([
_get_aligned_sentences(root_cell, "row"),
_get_aligned_sentences(root_cell, "col"),
]):
row_diff = min_row_diff(sentence, root_cell, absolute=False)
col_diff = min_col_diff(sentence, root_cell, absolute=False)
if ((row_diff or col_diff) and not (row_diff and col_diff)
and abs(row_diff) + abs(col_diff) <= dist):
if directions:
direction = ""
if col_diff == 0:
if 0 < row_diff and row_diff <= dist:
direction = "UP"
elif 0 > row_diff and row_diff >= -dist:
direction = "DOWN"
elif row_diff == 0:
if 0 < col_diff and col_diff <= dist:
direction = "RIGHT"
elif 0 > col_diff and col_diff >= -dist:
direction = "LEFT"
for ngram in tokens_to_ngrams(
getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower,
):
yield (ngram, direction)
else:
for ngram in tokens_to_ngrams(
getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower,
):
yield ngram
def get_neighbor_cell_ngrams(
mention: Union[Candidate, Mention, TemporarySpanMention],
dist: int = 1,
directions: bool = False,
attrib: str = "words",
n_min: int = 1,
n_max: int = 1,
lower: bool = True,
) -> Iterator[Union[str, Tuple[str, str]]]:
"""Get ngrams from all neighbor Cells.
Get the ngrams from all Cells that are within a given Cell distance in one
direction from the given Mention.
Note that if a candidate is passed in, all of its Mentions will be
searched. If `directions=True``, each ngram will be returned with a
direction in {'UP', 'DOWN', 'LEFT', 'RIGHT'}.
Also note that if the mention is not tabular, nothing will be yielded.
:param mention: The Mention whose neighbor Cells are being searched
:param dist: The Cell distance within which a neighbor Cell must be to be
considered
:param directions: A Boolean expressing whether or not to return the
direction of each ngram
:param attrib: The token attribute type (e.g. words, lemmas, poses)
:param n_min: The minimum n of the ngrams that should be returned
:param n_max: The maximum n of the ngrams that should be returned
:param lower: If True, all ngrams will be returned in lower case
:return: a *generator* of ngrams (or (ngram, direction) tuples if directions=True)
"""
# TODO: Fix this to be more efficient (optimize with SQL query)
spans = _to_spans(mention)
for span in spans:
if not span.sentence.is_tabular():
continue
for ngram in get_sentence_ngrams(span,
attrib=attrib,
n_min=n_min,
n_max=n_max,
lower=lower):
yield ngram
root_cell = span.sentence.cell
for sentence in chain.from_iterable([
_get_aligned_sentences(root_cell, "row"),
_get_aligned_sentences(root_cell, "col"),
]):
row_diff = min_row_diff([sentence, root_cell], absolute=False)
col_diff = min_col_diff([sentence, root_cell], absolute=False)
if (row_diff ^ col_diff # Exclusive OR
and abs(row_diff) + abs(col_diff) <= dist):
if directions:
if col_diff == 0:
direction = "DOWN" if 0 < row_diff else "UP"
else:
direction = "RIGHT" if 0 < col_diff else "LEFT"
for ngram in tokens_to_ngrams(
getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower,
):
yield (ngram, direction)
else:
for ngram in tokens_to_ngrams(
getattr(sentence, attrib),
n_min=n_min,
n_max=n_max,
lower=lower,
):
yield ngram
