class ExtractEntitiesStep(AbstractStep):
"""
Extract entities from collected text.
"""
def __init__(self,
name: str,
source_key: str = None,
overwrite: bool = False,
source_iter: Callable[[List[str]],
Iterator[IO[AnyStr]]] = file_iter,
output_handler: Callable[[str, Dict[str, Any]], None] = oh):
super().__init__(name, source_key, overwrite)
self.__source_iter = source_iter
self.__output_handler = output_handler
root_path = Path(__file__).parent.parent
entities_path = str(root_path / 'config/entities.csv')
self.entity_reverse_lookup, synonyms, self.regexprs = load_entities(
entities_path)
self.keyword_processor = prepare_keyword_processor(synonyms)
duckling_entities = {ENTITY_DATE, ENTITY_NUMBER}
tagger_entities = {ENTITY_PERSON}
if len(duckling_entities.intersection(ENABLED_SYSTEM_ENTITIES)) > 0:
self.d = DucklingWrapper()
if len(tagger_entities.intersection(ENABLED_SYSTEM_ENTITIES)) > 0:
self.tagger = SequenceTagger.load('ner')
def process_file(self, file: IO[AnyStr], path: str,
control_data: Dict[str, Any], logger: Logger,
accumulator: Dict[str, Any]) -> None:
logger.debug('process file: {}'.format(file.name))
input_doc = json.load(file)
metadata = input_doc['metadata']
record_id = metadata['record_id']
data = input_doc['data']
text = data['text']
nlp_text = []
for t in text:
entities = []
keywords_found = self.keyword_processor.extract_keywords(
t, span_info=True)
for keyword in keywords_found:
entities.append({
'entity':
self.entity_reverse_lookup[keyword[0]],
'location':
keyword[1:],
'value':
keyword[0],
'confidence':
1.0
})
matches = match_regexprs(t, self.regexprs)
for match in matches:
match['entity'] = self.entity_reverse_lookup[match['value']]
entities.extend(matches)
entities.extend(self.match_system_entities(t))
# is the span of an entity contained within the span
# of another entity
def is_contained(entity):
start, end = entity['location']
for ent in entities:
s, e = ent['location']
# exclude exact span matches
if (start == s and end < e) or (
start > s and end == e) or (start > s and end < e):
return True
return False
def is_valid(entity):
# remove spurious dates
if entity['entity'] == 'sys-date':
start, end = entity['location']
if (end - start) < 8:
return False
value = entity['value']
if isinstance(value, str):
try:
date = parse(value)
except ValueError:
return False
year = date.year
if year < 1990 or year > 2025:
return False
return True
# keep the entity with the longest span where an entity
# is contained within the span of another
pruned_entities = [
ent for ent in entities
if not is_contained(ent) and is_valid(ent)
]
nlp_text.append({'text': t, 'entities': pruned_entities})
now = datetime.utcnow().isoformat()
write_root_dir = control_data['job']['write_root_dir']
step_name = convert_name_to_underscore(self.name)
output_filename = '{}_{}.json'.format(step_name, record_id)
output_path = os.path.join(write_root_dir, step_name, output_filename)
data = {}
data['nlp_text'] = nlp_text
content = {'metadata': metadata, 'data': data}
accumulator['files_output'].append({
'filename': output_filename,
'input': path,
'path': output_path,
'status': 'processed',
'time': now
})
self.__output_handler(output_path, content)
def run(self, control_data: Dict[str, Any], logger: Logger,
accumulator: Dict[str, Any]) -> None:
file_paths = [x['path'] for x in control_data[self.source_key]]
step_name = convert_name_to_underscore(self.name)
processed_file_paths = {}
if step_name in control_data:
for x in control_data[step_name]:
if x['status'] == 'processed':
processed_file_paths[x['input']] = x
for file, path in self.__source_iter(file_paths):
if not self._overwrite and path in processed_file_paths.keys():
accumulator['files_output'].append(processed_file_paths[path])
continue
self.process_file(file, path, control_data, logger, accumulator)
def match_system_entities(self, utter):
matches = []
if ENTITY_DATE in ENABLED_SYSTEM_ENTITIES:
results = self.d.parse_time(utter)
for result in results:
matches.append({
'entity': 'sys-date',
'location': [result['start'], result['end']],
'value': result['value']['value'],
'confidence': 1.0
})
if ENTITY_NUMBER in ENABLED_SYSTEM_ENTITIES:
results = self.d.parse_number(utter)
for result in results:
matches.append({
'entity': 'sys-number',
'location': [result['start'], result['end']],
'value': result['value']['value'],
'confidence': 1.0
})
sentence = None
if ENTITY_PERSON in ENABLED_SYSTEM_ENTITIES:
if sentence is None:
sentence = Sentence(utter)
self.tagger.predict(sentence)
for entity in sentence.get_spans('ner'):
if entity.tag == 'PER':
matches.append({
'entity':
'sys-person',
'location': [entity.start_pos, entity.end_pos],
'value':
entity.text,
'confidence':
entity.score
})
return matches
ent['value'] = txt
ent['entity'] = 'product'
sentence['entities'].append(ent)
sentence['text'] += txt + " "
while random.random() > .5:
m = random.choice(middle)
sentence['text'] += m
txt = df.sample().iloc[0, 0]
ent = dict()
ent['start'] = len(sentence['text'])
ent['end'] = len(sentence['text'] + txt)
ent['value'] = txt
ent['entity'] = 'product'
sentence['entities'].append(ent)
sentence['text'] += txt + " "
sentence['text'] += random.choice(end)
train_data['rasa_nlu_data']["common_examples"].append(sentence)
with open('result.json', 'w+') as fp:
json.dump(train_data, fp)
container = IntentContainer('intent_cache')
d = DucklingWrapper()
d.parse('Bring me 250 ml sugar')
d.parse_
print(d.parse_time(u'Let\'s meet at 11:45am'))
print(d.parse_number(u'Bring me one conserve of ravioli'))
print(d.parse_quantity(u'Bring me 100 g of sugar'))
