def annotate(self, text):
for word in re.findall(self.regex_string, text):
yield Entity(word,
"Noun",
rules=[self.rule],
source_text=text,
confidence=0.8)
def annotate(self, text):
if self.lowercase:
words = text.lower().split()
else:
words = text.split()
conf = 0.75 if self.lowercase else 0.9
for word in words:
for city in self.cities:
code = city["country"]
name = city["name"]
if float(city["lat"]) < 0:
hemisphere = "south"
else:
hemisphere = "north"
data = {
"name": name,
"country_code": code,
"latitude": float(city["lat"]),
"longitude": float(city["lng"]),
"hemisphere": hemisphere
}
if self.lowercase:
name = name.lower()
if word == name:
yield Entity(data["name"],
"City",
source_text=text,
data=data,
confidence=conf)
def annotate(self, text):
for e in spotlight.annotate(self.host,
text,
confidence=self.confidence,
support=self.support):
for e_type in e["types"].split(","):
if e_type.startswith("DBpedia:"):
yield Entity(e["surfaceForm"],
e_type.split(":")[-1],
source_text=text,
data={
"uri":
e["URI"],
"support":
e["support"],
"offset":
e["offset"],
"percentageOfSecondRank":
e["percentageOfSecondRank"],
"similarityScore":
e["similarityScore"],
"types":
e["types"].split(",")
},
confidence=e["similarityScore"])
def extract_entities(self, text, as_json=False):
parsed = self.engine.parse(text)
for e in parsed:
yield Entity(e["value"],
e["entity_kind"],
source_text=text,
data=e["resolved_value"])
def add_entity_examples(self, name, examples):
if isinstance(examples, str):
examples = [examples]
self._container.add_entity(name, examples)
if name not in self._examples:
self._examples[name] = []
for e in examples:
self._examples[name].append(Entity(e, name))
def add_entity_examples(self, name, examples):
if isinstance(examples, str):
examples = [examples]
if name not in self._examples:
self._examples[name] = []
for e in examples:
rules = r'\b' + e.lower() + r"\b"
self._examples[name].append(
Entity(e, name, rules=Rule(name, rules)))
def annotate_duration(self, text):
delta, value = _annotate_duration_en(text)
if delta:
data = {
"days": delta.days,
"seconds": delta.seconds,
"microseconds": delta.microseconds,
"total_seconds": delta.total_seconds(),
"spoken": nice_duration(delta)
}
yield Entity(value, "duration", source_text=text, data=data)
def extract_entities(self, query):
for r in self._rules:
for rule in self._rules[r]:
for rul in rule.rules:
regex = self._create_regex(rul)
match = regex.findall(query)
for ent in match:
if not isinstance(ent, str):
ent = ent[0]
yield Entity(ent,
rule.name,
source_text=query,
rules=self._rules[r])
def annotate(self, text, pretokenized=False):
doc = self.pipeline.doc(text, pretokenized=pretokenized)
if doc is not None:
cons_list = []
if self.connl:
ents = doc.get_ner_conll
if ents.cons_list:
cons_list += ents.cons_list
elif self.ontonotes:
ents = doc.get_ner_ontonotes
if ents.cons_list:
cons_list += ents.cons_list
for e in cons_list:
yield Entity(e["tokens"], e["label"], source_text=text, data=e)
def annotate(self, text):
# deprecated
from dateparser.search import search_dates
matches = search_dates(text)
for value, date in matches:
data = {
"timestamp": date.timestamp(),
"isoformat": date.isoformat(),
"weekday": date.isoweekday(),
"month": date.month,
"day": date.day,
"hour": date.hour,
"minute": date.minute,
"year": date.year
}
yield Entity(value, "date", source_text=text, data=data)
def annotate(self, text):
words = nltk.word_tokenize(text)
tagged_Words = nltk.pos_tag(words)
named_Entity = nltk.ne_chunk(tagged_Words)
for x in named_Entity:
if isinstance(x, nltk.tree.Tree):
data = {
"label": x.__dict__["_label"],
"pos_tag": [e[1] for e in x],
"tokens": [e[0] for e in x]
}
yield Entity(" ".join([e[0] for e in x]),
x.__dict__["_label"],
source_text=text,
data=data)
def _old_annotate(self, text):
# deprecated
import datefinder
matches = datefinder.find_dates(text, index=True)
for date, span in matches:
value = text[span[0]:span[1]].strip()
data = {
"timestamp": date.timestamp(),
"isoformat": date.isoformat(),
"weekday": date.isoweekday(),
"month": date.month,
"day": date.day,
"hour": date.hour,
"minute": date.minute,
"year": date.year
}
yield Entity(value, "date", source_text=text, data=data)
def annotate(self, text):
for e in parser.parse(text):
spoken = e.to_spoken()
data = e.__dict__
data["unit"] = e.unit.__dict__
data["unit"]["entity"] = e.unit["entity"].__dict__
e_type = e.unit["entity"]["uri"]
if e.unit["uri"] != e.unit["entity"]["uri"]:
e_type = e.unit["entity"]["uri"] + ":" + e.unit["uri"]
data["spoken"] = spoken
data.pop("span")
if data["unit"]["currency_code"] is None:
data["unit"].pop("currency_code")
data = data.copy()
data.pop("surface")
yield Entity(e.surface, e_type, source_text=text, data=data)
def annotate(self, text):
annotation = self.model.predict(text).__dict__["annotations"]
for entity in annotation["entities"]:
label, start, end, text = annotation["entities"][entity]
yield Entity(text, label, source_text=text)
def annotate(self, text):
for label in self.entities:
for ent in self.entities[label]:
utt = re.sub(r'\b' + ent + r"\b", "", text.lower())
if utt != text.lower():
yield Entity(ent, label, source_text=text)
def annotate(self, text):
for e in spacy_NER(text):
val = text[e["start"]:e["end"]]
yield Entity(val, e["label"], source_text=text)
def annotate(self, text):
for x in self.rake.run(text):
data = {"score": x[1]}
yield Entity(x[0], "keyword", source_text=text, data=data)
def extract_hitler(text):
if "hitler" in text.lower():
yield Entity("hitler", "bad_guy", source_text=text, data={
"known_for": ["killing jews", "world war 2"]})
def annotate(self, text):
for e in polyglot_NER(text):
yield Entity(e[0], e[1], source_text=text)
def annotate(self, text):
doc = self.nlp(text)
for entity in doc.ents:
yield Entity(entity.text, entity.label_, source_text=text)
