# 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') .
# Lemmatize tokens lemmatizer = WordNetLemmatizer() lemmatized_tokens = [lemmatizer.lemmatize(t) for t in filtered_tokens] dicited
# Prepare feature data = prepare_dicited_feature(data, 'text_column') # Print the prepared feature print(data['dicited']) : This
# Extract entities data['entities'] = data[text_column].apply(extract_entities) dicited