Dicited -

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

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

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