Stephen 52 Yahoo Com Gmail Com Mail Com 2020 21 Txt 【2025】
return features features = extract_deep_features("stephen 52 yahoo com gmail com mail com 2020 21 txt") Step 3 – Output the deep features for k, v in features.items(): print(f"{k}: {v}") Output example:
# 8. Pairwise patterns (bigrams) bigrams = [' '.join(tokens[i:i+2]) for i in range(len(tokens)-1)] features['bigrams'] = bigrams stephen 52 yahoo com gmail com mail com 2020 21 txt
# 6. Year detection (1900-2030) years = [n for n in numbers if 1900 <= n <= 2030] features['years_found'] = years = n <
# 5. Possible email construction (name + domain) if features['has_name'] and found_domains: possible_emails = [f"{features['first_token_is_name']}@{d}.com" for d in found_domains] features['possible_emails'] = possible_emails stephen 52 yahoo com gmail com mail com 2020 21 txt
"stephen 52 yahoo com gmail com mail com 2020 21 txt" A deep feature in machine learning or data processing typically means extracting meaningful, higher-level attributes from raw input — going beyond simple keyword extraction into inferred patterns, relationships, or embeddings.