def select_summary_sentences(nlp, intro, abstract, filter_level):
if not (intro and abstract):
return ""
if filter_level <= 1:
return abstract
doc = nlp(abstract)
en_count_in_summary = 0
sentences_select = {}
for sent in doc.sents:
sentences_select[sent.text] = True
for e in doc.ents:
if e[0].ent_type_ in TRACKING_ENTITY_LIST:
en_count_in_summary += 1
# if e.text in source:
match_result = entity_match(e.text, intro, 2)
if not match_result:
sentences_select[e.sent.text] = False
# print("ENTITY NOT FOUND: {}".format(e.text))
# print(">>source>>", intro)
# print(">>summary>>", abstract)
# break
# if select and en_count_in_summary>0:
result = []
for sent in doc.sents:
if sentences_select[sent.text]:
result.append(sent.text)
return " ".join(result)
def select_example(nlp, intro, abstract, filter_level):
if not (intro and abstract):
return False
if filter_level <= 1:
return True
doc = nlp(abstract)
en_count_in_summary = 0
select = True
for e in doc.ents:
if e[0].ent_type_ in TRACKING_ENTITY_LIST:
en_count_in_summary += 1
# if e.text in source:
match_result = entity_match(e.text, intro, 2)
if not match_result:
select = False
# print("ENTITY NOT FOUND: {}".format(e.text))
# print(">>source>>", intro)
# print(">>summary>>", abstract)
break
# if select and en_count_in_summary>0:
if select:
return True
else:
return False
def ent_count_match(nlp, base, parent, is_scispacy=False):
# perform NER on base, then match in parant:
doc = nlp(base)
ent_count_base = 0
en_count_in_base_parent = 0
if is_scispacy:
for e in doc.ents:
ent_count_base += 1
# if e.text in source:
match_result = entity_match(e.text, parent, 1)
if match_result:
en_count_in_base_parent += 1
else:
for e in doc.ents:
if e[0].ent_type_ in TRACKING_ENTITY_LIST:
ent_count_base += 1
# if e.text in source:
match_result = entity_match(e.text, parent, 2)
if match_result:
en_count_in_base_parent += 1
return ent_count_base, en_count_in_base_parent
def create_ent_augmented_target(source_file,
target_file,
out_text_file,
out_bpe_file,
tokenizer_dir,
special_token=50009,
max_len=1024):
n_s = count_lines_in_text_file(source_file)
n_t = count_lines_in_text_file(target_file)
assert n_s == n_t, \
"Number of lines not consistent: {}, {}".format(n_s, n_t)
nlp = spacy.load("en_core_web_lg")
encoder_args = SimpleNamespace(
encoder_json=os.path.join(tokenizer_dir, "encoder.json"),
vocab_bpe=os.path.join(tokenizer_dir, "vocab.bpe"),
keep_empty=True)
bpe = get_encoder(encoder_args.encoder_json, encoder_args.vocab_bpe)
with open(source_file, 'r') as s_f, \
open(target_file, 'r') as t_f, \
open(out_bpe_file, 'w') as out_bpe_f, \
open(out_text_file, 'w') as out_text_f:
for _ in tqdm(range(n_s)):
sline = s_f.readline().strip()
tline = t_f.readline().strip()
doc = nlp(tline)
entities_per_example = []
for e in doc.ents:
if e[0].ent_type_ in TRACKING_ENTITY_LIST:
# if e.text in source:
match_result = entity_match(e.text, sline, 2)
if match_result:
entities_per_example.append(match_result[0])
target_bpe = bpe.encode(tline)
if entities_per_example:
entity_bpe = bpe.encode(", ".join(entities_per_example))
augmented_target_bpe = entity_bpe + [
special_token,
] + target_bpe
else:
augmented_target_bpe = [
special_token,
] + target_bpe
out_text_f.write("{}".format(entities_per_example) + '\n')
out_bpe_f.write(
' '.join(map(str, augmented_target_bpe[:max_len - 1])) + '\n')
def create_ent_labels(source_file,
target_file,
out_file,
tokenizer_dir,
first_only=False):
n_s = count_lines_in_text_file(source_file)
n_t = count_lines_in_text_file(target_file)
assert n_s == n_t, \
"Number of lines not consistent: {}, {}".format(n_s, n_t)
nlp = spacy.load("en_core_web_lg")
entities_found = []
encoder_args = SimpleNamespace(
encoder_json=os.path.join(tokenizer_dir, "encoder.json"),
vocab_bpe=os.path.join(tokenizer_dir, "vocab.bpe"),
keep_empty=True)
bpe = get_encoder(encoder_args.encoder_json, encoder_args.vocab_bpe)
with open(source_file, 'r') as s_f, \
open(target_file, 'r') as t_f, \
open(out_file, 'w') as out_f:
for _ in tqdm(range(n_s)):
sline = s_f.readline().strip()
tline = t_f.readline().strip()
tokens = bpe.encode(sline)
labels = [0] * len(tokens)
doc = nlp(tline)
entities_per_example = []
for e in doc.ents:
if e[0].ent_type_ in TRACKING_ENTITY_LIST:
entity_new = {'text': e.text, 'type': e[0].ent_type_}
# if e.text in source:
match_result = entity_match(e.text, sline, 2)
entity_new['match_result'] = match_result
labels = update_bio_labels(labels,
sline,
match_result,
tokens,
bpe,
first_only=first_only)
entities_per_example.append(entity_new)
out_f.write(" ".join([str(i) for i in labels]) + '\n')
entities_found.append(entities_per_example)
return entities_found