def processOneFile_fda(fileName, annotation_dir, nlp_tool, isTraining, types,
type_filter, isFDA2018, isNorm):
documents = []
annotation_file = get_fda_file(join(annotation_dir, fileName))
# each section is a document
for section in annotation_file.sections:
document = Document()
document.name = fileName[:fileName.find('.')] + "_" + section.id
if section.text is None:
document.text = ""
document.entities = []
document.sentences = []
documents.append(document)
continue
document.text = section.text
entities = []
if isFDA2018 == False and isNorm == True:
for reaction in annotation_file.reactions:
entity = Entity()
entity.name = reaction.name
for normalization in reaction.normalizations:
entity.norm_ids.append(
normalization.meddra_pt_id) # can be none
entity.norm_names.append(normalization.meddra_pt)
entities.append(entity)
else:
for entity in annotation_file.mentions:
if entity.section != section.id:
continue
if types and (entity.type not in type_filter):
continue
entities.append(entity)
document.entities = entities
if opt.nlp_tool == "nltk":
if isTraining:
sentences = get_sentences_and_tokens_from_nltk(
section.text, nlp_tool, document.entities,
annotation_file.ignore_regions, section.id)
else:
sentences = get_sentences_and_tokens_from_nltk(
section.text, nlp_tool, None,
annotation_file.ignore_regions, section.id)
else:
raise RuntimeError("invalid nlp tool")
document.sentences = sentences
documents.append(document)
return documents, annotation_file
def processOneFile_fda(fileName, annotation_dir, types, type_filter, isFDA2018,
isNorm):
documents = []
annotation_file = get_fda_file(os.path.join(annotation_dir, fileName))
# each section is a document
for section in annotation_file.sections:
document = Document()
document.name = fileName[:fileName.find('.')] + "_" + section.id
if section.text is None:
document.text = ""
document.entities = []
document.sentences = []
documents.append(document)
continue
document.text = section.text
entities = []
if isFDA2018 == False and isNorm == True:
for reaction in annotation_file.reactions:
entity = Entity()
entity.name = reaction.name
for normalization in reaction.normalizations:
entity.norm_ids.append(
normalization.meddra_pt_id) # can be none
entity.norm_names.append(normalization.meddra_pt)
entities.append(entity)
else:
for entity in annotation_file.mentions:
if entity.section != section.id:
continue
if types and (entity.type not in type_filter):
continue
entities.append(entity)
document.entities = entities
document.sentences = []
documents.append(document)
return documents, annotation_file
def parse_one_gold_file(annotation_dir, corpus_dir, fileName):
document = Document()
document.name = fileName[:fileName.find('.')]
annotation_file = get_bioc_file(os.path.join(annotation_dir, fileName))
bioc_passage = annotation_file[0].passages[0]
entities = []
for entity in bioc_passage.annotations:
if entity.infons['type'] not in type_we_care:
continue
entity_ = Entity()
entity_.id = entity.id
processed_name = entity.text.replace('\\n', ' ')
if len(processed_name) == 0:
logging.debug("{}: entity {} name is empty".format(
fileName, entity.id))
continue
entity_.name = processed_name
entity_.type = entity.infons['type']
entity_.spans.append(
[entity.locations[0].offset, entity.locations[0].end])
if ('SNOMED code' in entity.infons and entity.infons['SNOMED code'] != 'N/A') \
and ('SNOMED term' in entity.infons and entity.infons['SNOMED term'] != 'N/A'):
entity_.norm_ids.append(entity.infons['SNOMED code'])
entity_.norm_names.append(entity.infons['SNOMED term'])
elif ('MedDRA code' in entity.infons and entity.infons['MedDRA code'] != 'N/A') \
and ('MedDRA term' in entity.infons and entity.infons['MedDRA term'] != 'N/A'):
entity_.norm_ids.append(entity.infons['MedDRA code'])
entity_.norm_names.append(entity.infons['MedDRA term'])
else:
logging.debug("{}: no norm id in entity {}".format(
fileName, entity.id))
# some entities may have no norm id
continue
entities.append(entity_)
document.entities = entities
corpus_file = get_text_file(
os.path.join(corpus_dir,
fileName.split('.bioc')[0]))
document.text = corpus_file
return document
def processOneFile(fileName, annotation_dir, corpus_dir, nlp_tool, isTraining,
types, type_filter):
document = Document()
document.name = fileName[:fileName.find('.')]
ct_snomed = 0
ct_meddra = 0
ct_unnormed = 0
if annotation_dir:
annotation_file = get_bioc_file(join(annotation_dir, fileName))
bioc_passage = annotation_file[0].passages[0]
entities = []
for entity in bioc_passage.annotations:
if types and (entity.infons['type'] not in type_filter):
continue
entity_ = Entity()
entity_.id = entity.id
processed_name = entity.text.replace('\\n', ' ')
if len(processed_name) == 0:
logging.debug("{}: entity {} name is empty".format(
fileName, entity.id))
continue
entity_.name = processed_name
entity_.type = entity.infons['type']
entity_.spans.append(
[entity.locations[0].offset, entity.locations[0].end])
if ('SNOMED code' in entity.infons and entity.infons['SNOMED code'] != 'N/A')\
and ('SNOMED term' in entity.infons and entity.infons['SNOMED term'] != 'N/A'):
entity_.norm_ids.append(entity.infons['SNOMED code'])
entity_.norm_names.append(entity.infons['SNOMED term'])
ct_snomed += 1
elif ('MedDRA code' in entity.infons and entity.infons['MedDRA code'] != 'N/A')\
and ('MedDRA term' in entity.infons and entity.infons['MedDRA term'] != 'N/A'):
entity_.norm_ids.append(entity.infons['MedDRA code'])
entity_.norm_names.append(entity.infons['MedDRA term'])
ct_meddra += 1
else:
logging.debug("{}: no norm id in entity {}".format(
fileName, entity.id))
ct_unnormed += 1
continue
entities.append(entity_)
document.entities = entities
corpus_file = get_text_file(join(corpus_dir, fileName.split('.bioc')[0]))
document.text = corpus_file
if opt.nlp_tool == "spacy":
if isTraining:
sentences = get_sentences_and_tokens_from_spacy(
corpus_file, nlp_tool, document.entities)
else:
sentences = get_sentences_and_tokens_from_spacy(
corpus_file, nlp_tool, None)
elif opt.nlp_tool == "nltk":
if isTraining:
sentences = get_sentences_and_tokens_from_nltk(
corpus_file, nlp_tool, document.entities, None, None)
else:
sentences = get_sentences_and_tokens_from_nltk(
corpus_file, nlp_tool, None, None, None)
elif opt.nlp_tool == "stanford":
if isTraining:
sentences = get_sentences_and_tokens_from_stanford(
corpus_file, nlp_tool, document.entities)
else:
sentences = get_sentences_and_tokens_from_stanford(
corpus_file, nlp_tool, None)
else:
raise RuntimeError("invalid nlp tool")
document.sentences = sentences
return document, ct_snomed, ct_meddra, ct_unnormed
def load_data_pubtator(file_path):
# stat
ct_doc = 0
ct_entity = 0
documents = []
with codecs.open(file_path, 'r', 'UTF-8') as fp:
document = None
for line in fp:
line = line.strip()
if line == '':
if document is None:
continue
else:
# save the document
documents.append(document)
document = None
ct_doc += 1
elif line.find('|t|') != -1:
# a new document
document = Document()
columns = line.split('|t|')
document.name = columns[0]
document.text = columns[1] + " " # offset need + 1
elif line.find('|a|') != -1:
columns = line.split('|a|')
document.text += columns[1]
generator = nlp_tool.span_tokenize(document.text)
for t in generator:
document.all_sents_inds.append(t)
for ind in range(len(document.all_sents_inds)):
t_start = document.all_sents_inds[ind][0]
t_end = document.all_sents_inds[ind][1]
tmp_tokens = FoxTokenizer.tokenize(
t_start, document.text[t_start:t_end], False)
sentence_tokens = []
for token_idx, token in enumerate(tmp_tokens):
token_dict = {}
token_dict['start'], token_dict['end'] = token[
1], token[2]
token_dict['text'] = token[0]
sentence_tokens.append(token_dict)
document.sentences.append(sentence_tokens)
else:
columns = line.split('\t')
if columns[1] == 'CID': # for cdr corpus, we ignore relation
continue
if columns[4].find(
"Chemical"
) != -1: # for cdr corpus, we ignore chemical
continue
entity = Entity()
entity.spans.append([int(columns[1]), int(columns[2])])
entity.name = columns[3]
entity.type = columns[4]
if columns[5].find('|') != -1:
ids = columns[5].split('|')
for id in ids:
if id == '-1':
raise RuntimeError("id == -1")
if id.find("OMIM:") != -1:
id = id[id.find("OMIM:") + len("OMIM:"):]
entity.norm_ids.append(id)
else:
entity.norm_ids.append(id)
elif columns[5].find('+') != -1:
ids = columns[5].split('+')
for id in ids:
if id == '-1':
raise RuntimeError("id == -1")
if id.find("OMIM:") != -1:
id = id[id.find("OMIM:") + len("OMIM:"):]
entity.norm_ids.append(id)
else:
entity.norm_ids.append(id)
else:
id = columns[5]
if id.find("OMIM:") != -1:
id = id[id.find("OMIM:") + len("OMIM:"):]
entity.norm_ids.append(id)
else:
entity.norm_ids.append(id)
# columns[6], cdr may has Individual mentions, we don't use it yet
for sent_idx, (sent_start,
sent_end) in enumerate(document.all_sents_inds):
if entity.spans[0][0] >= sent_start and entity.spans[0][
1] <= sent_end: # we assume entity has only one span
entity.sent_idx = sent_idx
break
if entity.sent_idx == -1:
logging.debug("can't find entity.sent_idx: {} ".format(
entity.name))
continue
# raise RuntimeError("can't find entity.sent_idx")
tkStart = -1
tkEnd = -1
for tkidx, token_dict in enumerate(
document.sentences[entity.sent_idx]):
if token_dict['start'] == entity.spans[0][0]:
tkStart = tkidx
if token_dict['end'] == entity.spans[0][1]:
tkEnd = tkidx
if tkStart != -1 and tkEnd != -1:
break
if tkStart == -1 or tkEnd == -1:
raise RuntimeError('tkStart == -1 or tkEnd == -1')
entity.tkSpans.append([tkStart, tkEnd])
document.entities.append(entity)
ct_entity += 1
logging.info("document number {}, entity number {}".format(
ct_doc, ct_entity))
return documents