Dicited

# Lemmatize tokens lemmatizer = WordNetLemmatizer() lemmatized_tokens = [lemmatizer.lemmatize(t) for t in filtered_tokens]

# Join tokens back into a string preprocessed_text = ' '.join(lemmatized_tokens) return preprocessed_text def extract_entities(text): """Extract entities from text data using spaCy.""" nlp = spacy.load('en_core_web_sm') doc = nlp(text) entities = [(ent.text, ent.label_) for ent in doc.ents] return entities Prepare Feature def prepare_dicited_feature(data, text_column): """Prepare the 'dicited' feature by preprocessing text data and extracting entities.""" # Preprocess text data data['preprocessed_text'] = data[text_column].apply(preprocess_text) dicited

# Prepare feature data = prepare_dicited_feature(data, 'text_column') dicited

# Extract entities data['entities'] = data[text_column].apply(extract_entities) dicited

# Print the prepared feature print(data['dicited']) : This is a basic example, and you may want to fine-tune the preprocessing and entity recognition steps based on your specific use case. Additionally, you will need to download the required NLTK data using nltk.download('punkt') and nltk.download('stopwords') .

# Remove stopwords stop_words = set(stopwords.words('english')) filtered_tokens = [t for t in tokens if t.lower() not in stop_words]