python
def preprocess_text(text):
text = re.sub(r'[^\w\s]', '', text)
text = remove_stopwords(text)
return text
def remove_stopwords(text):
filtered_text = [word for word in text.split() if word not in stopwords]
return ' '.join(filtered_text)
python
def train_annotation_model(train_data):
crf_model = CRF()
crf_model.fit(train_data)
return crf_model
def detect_annotations(text, model):
annotations = model.predict(text)
return annotations
python
def extract_named_entities(text, annotations):
named_entities = []
for annotation in annotations:
if annotation.type == 'NER':
named_entity = text[annotation.start: annotation.end]
named_entities.append(named_entity)
return named_entities
