Dicited 2021 -

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

# 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

# Prepare feature data = prepare_dicited_feature(data, 'text_column') # Extract entities data['entities'] = data[text_column]

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

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

# 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)

# Create a new feature 'dicited' that combines preprocessed text and entities data['dicited'] = data.apply(lambda row: (row['preprocessed_text'], row['entities']), axis=1) return data # Load data data = load_data('text_data.csv')