langchain_experimental.data_anonymizer.deanonymizer_matching_strategies.combined_exact_fuzzy_matching_strategy

langchain_experimental.data_anonymizer.deanonymizer_matching_strategies.combined_exact_fuzzy_matching_strategy(text: str, deanonymizer_mapping: Dict[str, Dict[str, str]], max_l_dist: int = 3) → str

Combined exact and fuzzy matching strategy for deanonymization.

It is a RECOMMENDED STRATEGY.

Parameters

• text (str) – text to deanonymize

• deanonymizer_mapping (Dict[str, Dict[str, str]]) – mapping between anonymized entities and original ones

• max_l_dist (int) – maximum Levenshtein distance between the anonymized entity and the text segment to consider it a match

Return type

str

Examples of matching:

Kaenu Reves -> Keanu Reeves John F. Kennedy -> John Kennedy

Examples using combined_exact_fuzzy_matching_strategy

• Reversible data anonymization with Microsoft Presidio {#reversible-data-anonymization-with-microsoft-presidio}