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"""
This file provides many functionalities that can be shared among different components.
The most important function in this file is `chunk_annotate_and_merge_to_phrase` which recieves a model and a raw text,
annotates the text, and returns the annotation spans.
"""
import os
import json
import pickle
import string
from enum import Enum
from tqdm import tqdm
from data_loader import get_dataset, tokenizer, dl_sa
from span_annotation import SubwordAnnotation, WordAnnotation, PhraseAnnotation
from aida import AIDADataset
from configuration import get_resources_dir
from mosestokenizer import MosesTokenizer, MosesPunctuationNormalizer
moses_tokenize = MosesTokenizer('en', old_version=True)
normalize = MosesPunctuationNormalizer('en')
def get_punc_tokenized_words_list(word_list: list, labels_list: list = None):
tokens = []
labels = []
for w_ind, o_token in enumerate(word_list):
if o_token[0] not in string.punctuation and o_token[-1] not in string.punctuation:
tokens.append(o_token)
if labels_list:
labels.append(labels_list[w_ind])
if o_token.endswith("\'s") or o_token.endswith("\'S"):
tokens[-1] = tokens[-1][:-2]
tokens.append(o_token[-2:])
if labels_list:
labels.append(labels_list[w_ind])
continue
# cases where the tokens start or end with punctuation
before_tokens = []
after_tokens = []
while o_token and o_token[0] in string.punctuation:
before_tokens.append(o_token[0])
o_token = o_token[1:]
while o_token and o_token[-1] in string.punctuation:
after_tokens.append(o_token[-1])
o_token = o_token[:-1]
if before_tokens:
tokens.append("".join(before_tokens))
if labels_list:
labels.append(labels_list[w_ind])
if o_token:
tokens.append(o_token)
if labels_list:
labels.append(labels_list[w_ind])
if after_tokens:
tokens.append("".join(after_tokens[::-1]))
if labels_list:
labels.append(labels_list[w_ind])
if labels_list:
return tokens, labels
return tokens
def save_predictions_result(logdir, epoch, precision, recall, f1, num_proposed, num_correct, num_gold,
all_words, all_tags, all_y_hat, all_predicted):
final = logdir + "/%s.P%.2f_R%.2f_F%.2f" % ("{}".format(str(epoch)), precision, recall, f1,)
with open(final, "w") as fout:
for words, tags, y_hat, preds in zip(all_words, all_tags, all_y_hat, all_predicted):
assert len(preds) == len(words) == len(tags)
for w, t, p in zip(words, tags, preds):
if w == '<s>' or t == '<pad>':
continue
fout.write(f"{w}\t{t}\t{p}\n")
fout.write("\n")
fout.write(f"num_proposed={num_proposed}\n")
fout.write(f"num_correct={num_correct}\n")
fout.write(f"num_gold={num_gold}\n")
fout.write(f"precision={precision}\n")
fout.write(f"recall={recall}\n")
fout.write(f"f1={f1}\n")
def get_subword_to_word_mapping(subword_tokens, original_string, sequence_starts_and_ends_with_bos_eos=True):
# subword_tokens starts with <s> and ends with </s>
if sequence_starts_and_ends_with_bos_eos:
subword_tokens = subword_tokens[1:-1]
subword_to_word_mapping = []
start_subword_index = 0
end_subword_index = 0
original_tokens = get_punc_tokenized_words_list(original_string.split())
original_pointer = 0
while len(subword_to_word_mapping) != len(original_tokens):
next_t = tokenizer.convert_tokens_to_string(subword_tokens[start_subword_index:end_subword_index])
next_t = next_t.strip()
if next_t == original_tokens[original_pointer]:
subword_to_word_mapping.append((start_subword_index, end_subword_index))
original_pointer += 1
start_subword_index = end_subword_index
else:
end_subword_index += 1
if end_subword_index - start_subword_index > 1000:
for i in [0, 1, 2, 3, 4]:
n = tokenizer.convert_tokens_to_string(subword_tokens[start_subword_index:start_subword_index + 2 + i]).strip()
o = "".join(original_tokens[original_pointer: original_pointer + 2]).replace("`", "\'")
if n == o or n.replace(" ", "") == o.replace(" ", ""):
subword_to_word_mapping.append((start_subword_index, start_subword_index + 1))
original_pointer += 1
start_subword_index = start_subword_index + 1
subword_to_word_mapping.append((start_subword_index, start_subword_index + 1 + i))
original_pointer += 1
start_subword_index = start_subword_index + 1 + i
end_subword_index = start_subword_index
break
return subword_to_word_mapping
def store_validation_data_wiki(checkpoints_root, batch_size, label_size, is_training, use_retokenized_wikipedia_data):
dataset_name = f"validation_data_cache_b_{batch_size}_l_{label_size}_" \
f"{('rt_wiki' if use_retokenized_wikipedia_data else 'wiki') if is_training else 'conll'}/"
if not os.path.exists(os.path.join(checkpoints_root, dataset_name)):
os.mkdir(os.path.join(checkpoints_root, dataset_name))
else:
print("Retrieving the validation data ...")
return dataset_name
print("Caching the validation data ...")
if is_training:
valid_iter = tqdm(get_dataset(
dataset_name='enwiki', split='valid', batch_size=batch_size, label_size=label_size,
use_retokenized_wikipedia_data=use_retokenized_wikipedia_data))
else:
valid_iter = tqdm(get_dataset(dataset_name='aida', split='valid', batch_size=batch_size, label_size=label_size))
for ind, (inputs, subword_mentions) in enumerate(valid_iter):
with open(os.path.join(checkpoints_root, dataset_name, f"{ind}"), "wb") as store_file:
pickle.dump((inputs.token_ids.cpu(), subword_mentions.ids.cpu(), subword_mentions.probs.cpu(),
inputs.eval_mask.cpu(), subword_mentions.dictionary, inputs.raw_mentions,
inputs.is_in_mention.cpu(), inputs.bioes.cpu()), store_file,
protocol=pickle.HIGHEST_PROTOCOL)
return dataset_name
def postprocess_annotations(annotations, sentence):
res = []
for ann in annotations:
begin_index = ann[0]
end_index = ann[1]
annotation = ann[2]
requires_check = True
while requires_check and end_index > begin_index:
mention = sentence[begin_index:end_index]
if mention.lower().endswith("\'s") and all([any([m in c for c in annotation[0].lower().split("_")])
for m in mention[:-2].lower().split()]) and not \
all([any([m in c for c in annotation[0].lower().split("_")]) for m in mention.lower().split()]):
end_index -= 2
elif mention[0] in string.punctuation or mention[0] == ' ':
begin_index += 1
elif mention[-1] in string.punctuation and mention.lower()[-4:] not in ["u.s.", "u.n."]:
end_index -= 1
elif mention[-1] == ' ':
end_index -= 1
elif mention.lower()[-3:] in ["u.s", "u.n"] and end_index < len(sentence) and sentence[end_index] == '.':
end_index += 1
elif mention.lower() in ["a", "the", "in", "out", "to", "of", "for", "at", "by", "rd", "th", "and",
"or", "but", "on", "none", "is", "were", "was", "he", "she", "if", "as",
"have", "had", "has", "who", "when", "where", "a lot", "a little", "here",
"there", "\'s"]:
end_index = begin_index
requires_check = False
else:
requires_check = False
if begin_index < end_index:
res.append((begin_index, end_index, annotation))
return res
def get_aida_set_phrase_splitted_documents(dataset_name):
d_iter = AIDADataset().dataset[dataset_name]
phrase_documents = []
for document in d_iter:
document_words = []
document_labels = []
document_candidates = []
for annotation in document.annotations:
for a in annotation:
document_words.append(a.token)
document_candidates.append([x.url.replace('http://en.wikipedia.org/wiki/', '')
for x in a.candidates.candidates] if a.candidates else [])
if a.yago_entity and a.yago_entity != "--NME--":
document_labels.append(a.yago_entity.encode('ascii').decode('unicode-escape'))
else:
document_labels.append('|||O|||')
original_string = " ".join(document_words)
tokenized_mention = tokenizer(original_string)
tokens_offsets = list(zip(tokenized_mention.tokens(), tokenized_mention.encodings[0].offsets))[1:-1]
mapping = get_subword_to_word_mapping(tokenized_mention.tokens(), original_string)
subword_tokens = tokenized_mention.tokens()[1:-1]
w_ind = 0
subword_annotations = []
word_annotations = []
for w, l, cnds in zip(document_words, document_labels, document_candidates):
converted_to_words = "".join([x[1:] if x.startswith("\u0120")
else x for x in subword_tokens[mapping[w_ind][0]:mapping[w_ind][1]]])
if w == converted_to_words:
for sub_w in subword_tokens[mapping[w_ind][0]:mapping[w_ind][1]]:
subword_annotations.append(SubwordAnnotation([1.0], [dl_sa.mentions_vocab[l]], sub_w))
word_annotations.append(WordAnnotation(subword_annotations[mapping[w_ind][0]:mapping[w_ind][1]],
tokens_offsets[mapping[w_ind][0]:mapping[w_ind][1]], cnds))
w_ind += 1
elif len(mapping) > w_ind + 1 and w == "".join([x[1:] if x.startswith("\u0120")
else x for x in subword_tokens[
mapping[w_ind][0]:mapping[w_ind+1][1]]]):
for sub_w in subword_tokens[mapping[w_ind][0]:mapping[w_ind+1][1]]:
subword_annotations.append(SubwordAnnotation([1.0], [dl_sa.mentions_vocab[l]], sub_w))
word_annotations.append(WordAnnotation(subword_annotations[mapping[w_ind][0]:mapping[w_ind+1][1]],
tokens_offsets[mapping[w_ind][0]:mapping[w_ind+1][1]], cnds))
w_ind += 2
else:
raise ValueError("This should not happen")
phrase_annotations = []
for w in word_annotations:
if phrase_annotations and phrase_annotations[-1].resolved_annotation == w.resolved_annotation:
phrase_annotations[-1].add(w)
else:
phrase_annotations.append(PhraseAnnotation(w))
phrase_documents.append(phrase_annotations)
return phrase_documents
def _process_last_overlap(text_chunk_overlap, _overlap, l):
"""
Function intended to merge the predictions in the text chunk overlaps.
Implemented to be used in chunk_annotate_and_merge_to_phrase function.
"""
if not l:
l = _overlap
if len(l) < len(_overlap):
o = [x for x in _overlap]
o[-len(l):] = l
l = o
_r = []
if len(_overlap) < text_chunk_overlap:
text_chunk_overlap = len(_overlap)
for i in range(text_chunk_overlap):
if _overlap[i] == 0:
_r.append((l[i],))
elif l[i] == 0 or _overlap[i] == l[i]:
_r.append((_overlap[i],))
else: # keeping both for prediction resolution
_r.append((l[i], _overlap[i]))
return _r
def normalize_sentence_for_moses_alignment(sentence, normalize_for_chinese_characters=False):
for k, v in [('\u2018', '\''), ('\u2019', '\''), ('\u201d', '\"'), ('\u201c', '\"'), ('\u2013', '-'),
('\u2014', '-'), ('\u2026', '.'), ('\u2022', '.'), ('\u00f6', 'o'),('\u00e1', 'a'), ('\u00e8', 'e'),
('\u00c9', 'E'), ('\u014d', 'o'), ('\u0219', 's'), ('\n', '\u010a'), ('\u00a0', ' '), ('\u694a', ' '),
('\u9234', ' '), ('\u6797', ' '), ('\u6636', ' '), ('\u4f50', ' '), ('\u738b', ' '), ('\u5b9c', ' '),
('\u6b63', ' '), ('\u5168', ' '), ('\u52dd', ' '), ('\u80e1', ' '), ('\u5fd7', ' '), ('\u535a', ' '),
('\u9673', ' '), ('\u7f8e', ' '), ('\u20ac', 'E'), ('\u201e', '\"'), ('\u0107', 'c'), ('\ufeff', ' '),
('\u017e', 'z'), ('\u010d', 'c')]:
if k in sentence:
sentence = sentence.replace(k, v)
if normalize_for_chinese_characters:
for k, v in [('\u5e7c', ' '), ('\u5049', ' '), ('\u5b8f', ' '), ('\u9054', ' '), ('\u5bb9', ' '),
('\u96fb', ' '), ('\u590f', ' '), ('\u5b63', ' '), ('\u660c', ' '), ('\u90b1', ' '),
('\u4fca', ' '), ('\u6587', ' '), ('\u56b4', ' '), ('\u5b87', ' '), ('\u67cf', ' '),
('\u8b5a', ' '), ('\u9f0e', ' '), ('\u6176', ' '), ('\u99ac', ' '), ('\u82f1', ' '),
('\u4e5d', ' '), ('\u6797', ' '), ('\u7537', ' '), ('\u9996', ' '), ('\u60e0', ' '),
('\u7d00', ' '), ('\u5143', ' '), ('\u8f1d', ' '), ('\u5289', ' '), ('\u4fd0', ' '),
('\u8208', ' '), ('\u4e2d', ' '), ('\u8b1d', ' '), ('\u5922', ' '), ('\u9e9f', ' '),
('\u6e38', ' '), ('\u570b', ' '), ('\u7167', ' '), ('\u658c', ' '), ('\u54f2', ' '),
('\u9ec3', ' '), ('\u5433', ' '), ('\u53cb', ' '), ('\u6e05', ' '), ('\u856d', ' '),
('\u8000', ' '), ('\u5eb7', ' '), ('\u6dd1', ' '), ('\u83ef', ' ')]:
if k in sentence:
sentence = sentence.replace(k, v)
return sentence
def chunk_annotate_and_merge_to_phrase(model, sentence, k_for_top_k_to_keep=5, normalize_for_chinese_characters=False):
sentence = sentence.rstrip()
sentence = normalize_sentence_for_moses_alignment(sentence, normalize_for_chinese_characters)
simple_split_words = moses_tokenize(sentence)
sentence = sentence.replace('\u010a', '\n')
tokenized_mention = tokenizer(sentence)
tokens_offsets = list(zip(tokenized_mention.tokens(), tokenized_mention.encodings[0].offsets))
subword_to_word_mapping = get_subword_to_word_mapping(tokenized_mention.tokens(), sentence)
chunks = [tokens_offsets[i: i + model.text_chunk_length] for i in range(
0, len(tokens_offsets), model.text_chunk_length - model.text_chunk_overlap)]
result = []
last_overlap = []
logits = []
# ########################################################################################################
# Covert each chunk to tensors, predict the labels, and merge the overlaps (keep conflicting predictions).
# ########################################################################################################
for chunk in chunks:
subword_ids = [tokenizer.convert_tokens_to_ids([x[0] for x in chunk])]
logits = model.annotate_subword_ids(
subword_ids, k_for_top_k_to_keep, chunk)
if last_overlap:
result.extend(_process_last_overlap(model.text_chunk_overlap, last_overlap, logits))
else:
result.extend([(x,) for x in logits[:model.text_chunk_overlap]])
if len(logits) > 2 * model.text_chunk_overlap:
result.extend([(x,) for x in logits[model.text_chunk_overlap:-model.text_chunk_overlap]])
last_overlap = logits[-model.text_chunk_overlap:]
else:
result.extend([(x,) for x in logits[model.text_chunk_overlap:]])
last_overlap = []
logits = []
result.extend(_process_last_overlap(model.text_chunk_overlap, last_overlap, logits))
# ########################################################################################################
# Resolve the overlap merge conflicts using the model prediction probability
# ########################################################################################################
final_result = []
for p_ind, prediction in enumerate(result):
if len(prediction) == 1:
final_result.append(prediction[0])
else:
p_found = False
for p in prediction:
if p == final_result[-1] or (p_ind + 1 < len(result) and p in result[p_ind + 1]):
# It is equal to the one in the left or in the one to the right
final_result.append(p)
p_found = True
break
if not p_found: # choose the one the model is more confident about
final_result.append(sorted(prediction, key=lambda x: x.item_probability(), reverse=True)[0])
# ########################################################################################################
# Convert the model predictions (subword-level) to valid GERBIL annotation spans (continuous char-level)
# ########################################################################################################
tokens_offsets = tokens_offsets[1:-1]
final_result = final_result[1:]
# last_step_annotations = []
word_annotations = [WordAnnotation(final_result[m[0]:m[1]], tokens_offsets[m[0]:m[1]])
for m in subword_to_word_mapping]
# ########################################################################################################
# MAKING SURE WORDS ARE NOT BROKEN IN SEPARATE PHRASES!
# ########################################################################################################
w_p_1 = 0
w_p_2 = 0
w_2_buffer = ""
w_1_buffer = ""
while w_p_1 < len(word_annotations) and w_p_2 < len(simple_split_words):
w_1 = word_annotations[w_p_1]
w_2 = normalize(simple_split_words[w_p_2]).strip()
w_1_word_string = normalize(w_1.word_string).strip()
if w_1_word_string == w_2:
w_p_1 += 1
w_p_2 += 1
elif w_1_buffer and w_2_buffer and normalize(
w_1_buffer + w_1.word_string).strip() == normalize(w_2_buffer + simple_split_words[w_p_2]).strip():
w_p_1 += 1
w_p_2 += 1
w_1_buffer = ""
w_2_buffer = ""
elif w_2_buffer and w_1_word_string == normalize(w_2_buffer + simple_split_words[w_p_2]).strip():
w_p_1 += 1
w_p_2 += 1
w_2_buffer = ""
elif w_1_buffer and normalize(w_1_buffer + w_1.word_string).strip() == w_2:
w_p_1 += 1
w_p_2 += 1
w_1_buffer = ""
elif w_1_buffer and len(w_2) < len(normalize(w_1_buffer + w_1.word_string).strip()):
w_2_buffer += simple_split_words[w_p_2]
w_p_2 += 1
elif len(w_2) < len(w_1_word_string):
w_2_buffer += simple_split_words[w_p_2]
w_p_2 += 1
# Connecting the "." in between the names to the word it belongs to.
elif len(w_2) > len(w_1_word_string) and w_p_1 + 1 < len(word_annotations) \
and word_annotations[w_p_1 + 1].word_string == ".":
word_annotations[w_p_1 + 1] = WordAnnotation(
word_annotations[w_p_1].annotations + word_annotations[w_p_1 + 1].annotations,
word_annotations[w_p_1].token_offsets + word_annotations[w_p_1 + 1].token_offsets)
word_annotations[w_p_1].annotations = []
word_annotations[w_p_1].token_offsets = []
w_p_1 += 1
elif len(w_2) > len(w_1_word_string) and w_p_1 + 1 < len(word_annotations):
w_1_buffer += w_1.word_string
w_p_1 += 1
elif w_2_buffer and normalize(word_annotations[w_p_1].word_string + word_annotations[w_p_1 + 1].word_string).strip():
w_p_1 += 2
w_2_buffer = ""
else:
raise ValueError("This should not happen!")
# ################################################################################################################
phrase_annotations = []
for w in word_annotations:
if not w.annotations:
continue
if phrase_annotations and phrase_annotations[-1].resolved_annotation == w.resolved_annotation:
phrase_annotations[-1].add(w)
else:
phrase_annotations.append(PhraseAnnotation(w))
return phrase_annotations
class ComparisonResult(Enum):
CORRECTLY_IGNORED_O = 0
CORRECTLY_FOUND_BOTH_SPAN_AND_ANNOTATION = 1
CORRECTLY_FOUND_SPAN_BUT_NOT_ANNOTATION = 2
OVER_GENERATED_ANNOTATION = 3
@staticmethod
def get_correct_status(g_span, p_span):
g_is_o = g_span.resolved_annotation == 0
got_annotation_right = p_span.resolved_annotation == g_span.resolved_annotation
got_span_right = p_span.word_string.replace(" ", "") == g_span.word_string.replace(" ", "")
# p_span.average_annotation_confidence == g_span.average_annotation_confidence
if got_span_right and got_annotation_right and g_is_o:
return ComparisonResult.CORRECTLY_IGNORED_O
elif got_span_right and got_annotation_right and not g_is_o:
return ComparisonResult.CORRECTLY_FOUND_BOTH_SPAN_AND_ANNOTATION
elif got_span_right and not got_annotation_right and not g_is_o:
# it could be that p is o or not!
return ComparisonResult.CORRECTLY_FOUND_SPAN_BUT_NOT_ANNOTATION
elif got_span_right and not got_annotation_right and g_is_o:
return ComparisonResult.OVER_GENERATED_ANNOTATION
else:
raise ValueError("This should not happen!")
def compare_gold_and_predicted_annotation_documents(gold_document, predicted_document, ignore_over_generated=False,
ignore_predictions_outside_candidate_list=False):
"""
Compares the output results of the model predictions and the gold annotations.
"""
g_id = 0
p_id = 0
comparison_results = []
while g_id < len(gold_document) and p_id < len(predicted_document):
p_span = predicted_document[p_id]
g_span = gold_document[g_id]
special_condition = p_span.word_string != g_span.word_string and p_span.word_string.replace(
" ", "") == g_span.word_string.replace(" ", "")
if p_span.word_string == g_span.word_string or special_condition:
p_id += 1
g_id += 1
comparison_results.append((g_span, p_span, ComparisonResult.get_correct_status(g_span, p_span)))
elif len(p_span.word_string) < len(g_span.word_string) and \
len(p_span.words) == len(g_span.words) == 1 and p_id + 1 < len(predicted_document) and \
len(predicted_document[p_id+1].words) > 1:
p_span.add(predicted_document[p_id+1].words[0])
predicted_document[p_id+1].words.pop(0)
continue
elif len(p_span.word_string) < len(g_span.word_string):
# potentially over-generated span later
new_phrase = PhraseAnnotation(g_span.words[0])
i = 1
while new_phrase.word_string.replace(" ", "") != p_span.word_string.replace(" ", "") \
and i < len(g_span.words):
new_phrase.add(g_span.words[i])
i += 1
not_solved = new_phrase.word_string.replace(" ", "") != p_span.word_string.replace(" ", "")
if not_solved and p_id + 1 < len(predicted_document) and len(predicted_document[p_id+1].words) > 1:
p_span.add(predicted_document[p_id+1].words[0])
predicted_document[p_id+1].words.pop(0)
continue
elif not_solved and p_id + 1 < len(predicted_document) and len(predicted_document[p_id+1].words) == 1:
p_span.add(predicted_document[p_id+1].words[0])
predicted_document[p_id+1].words = p_span.words
predicted_document[p_id+1].set_alternative_as_resolved_annotation(p_span.resolved_annotation)
p_id += 1
continue
elif not_solved:
raise ValueError("This should not happen!")
else:
comparison_results.append((
new_phrase, p_span, ComparisonResult.get_correct_status(new_phrase, p_span)))
g_span.words = g_span.words[i:]
p_id += 1
elif len(p_span.word_string) > len(g_span.word_string):
# potentially missed a span
new_phrase = PhraseAnnotation(p_span.words[0])
i = 1
while new_phrase.word_string.replace(" ", "") != g_span.word_string.replace(" ", "") \
and i < len(p_span.words):
new_phrase.add(p_span.words[i])
i += 1
if new_phrase.word_string.replace(" ", "") != g_span.word_string.replace(" ", ""):
# re-alignment not helpful
new_p = PhraseAnnotation(p_span.words[0])
new_g = PhraseAnnotation(g_span.words[0])
i = 1
while new_p.word_string == new_g.word_string:
new_p.add(p_span.words[i])
new_g.add(g_span.words[i])
i += 1
new_p.words = new_p.words[:-1]
new_g.words = new_g.words[:-1]
comparison_results.append((new_g, new_p, ComparisonResult.get_correct_status(new_g, new_p)))
p_span.words = p_span.words[i - 1:]
g_span.words = g_span.words[i - 1:]
else:
comparison_results.append((
g_span, new_phrase, ComparisonResult.get_correct_status(g_span, new_phrase)))
p_span.words = p_span.words[i:]
g_id += 1
elif g_span.word_string.replace(" ", "").startswith(p_span.word_string.replace(" ", "")) and \
p_id + 1 < len(predicted_document) and p_span.word_string.replace(" ", "") + \
predicted_document[p_id + 1].word_string.replace(" ", "") == g_span.word_string.replace(" ", ""):
for next_span_word in predicted_document[p_id+1].words:
p_span.add(next_span_word)
predicted_document[p_id+1] = p_span
p_id += 1
continue
elif g_span.word_string.replace(" ", "").startswith(p_span.word_string.replace(" ", "")) and \
p_id + 1 < len(predicted_document) and p_span.word_string.replace(" ", "") + \
predicted_document[p_id + 1].words[0].word_string.replace(" ", "") == \
g_span.word_string.replace(" ", ""):
p_span.add(predicted_document[p_id+1].words[0])
predicted_document[p_id+1].words.pop(0)
continue
elif g_span.word_string.replace(" ", "").startswith(p_span.word_string.replace(" ", "")):
raise ValueError("This should be handled correctly!")
elif p_span.word_string.replace(" ", "").startswith(g_span.word_string.replace(" ", "")):
raise ValueError("This should be handled correctly!")
else:
raise ValueError("This should not happen!")
if ignore_over_generated:
c_results = []
for g, p, r in comparison_results:
if ignore_over_generated and r == ComparisonResult.OVER_GENERATED_ANNOTATION:
p.set_alternative_as_resolved_annotation(0)
r = ComparisonResult.CORRECTLY_IGNORED_O
c_results.append((g, p, r))
comparison_results = c_results
if ignore_predictions_outside_candidate_list:
c_results = []
for g, p, r in comparison_results:
g_ppr_for_ned_candidates = [dl_sa.mentions_vocab[x] for x in g.ppr_for_ned_candidates if x in dl_sa.mentions_vocab]
if g_ppr_for_ned_candidates:
all_p_anns = p.all_possible_annotations()
filtered_p_predictions = sorted([x for x in all_p_anns if x[0] in g_ppr_for_ned_candidates],
key=lambda y: y[1], reverse=True)
if filtered_p_predictions:
p.set_alternative_as_resolved_annotation(filtered_p_predictions[0][0])
else:
p.set_alternative_as_resolved_annotation(0)
r = ComparisonResult.get_correct_status(g, p)
c_results.append((g, p, r))
comparison_results = c_results
return comparison_results
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