Anonymize Named Entities in Text Using NLP

These functions use natural language processing to identify and anonymize different types of named entities from text. Each function combines NLP entity extraction with pattern expansion and anonymization.

Usage

anon_nlp_entities(x, entity_types = nlp_entity_sets$all, ...)

anon_nlp_proper_nouns(x, ...)

anon_nlp_dates(x, ...)

anon_nlp_dates_and_times(x, ...)

anon_nlp_named(x, ...)

anon_nlp_numbers(x, ...)

anon_nlp_organizations(x, ...)

anon_nlp_people(x, ...)

anon_nlp_places(x, ...)

Arguments

x

The object to anonymize. Can be a character vector, factor, data frame, or list.

entity_types

Character vector of entity types to extract. Entity types are: "PERSON", "NORP", "FAC", "ORG", "GPE", "LOC", "PRODUCT", "EVENT", "WORK_OF_ART", "LAW", "LANGUAGE", "DATE", "TIME", "PERCENT", "MONEY", "QUANTITY", "ORDINAL", "CARDINAL". See nlp_entity_sets for collections of entity types.

...

Additional arguments passed to anon

Value

An anonymized object of class anon_context with named entities replaced according to the anonymization workflow.

Details

These functions:

1. Use NLP to extract potentially sensitive information of the specified type

2. Extend patterns using more_patterns for comprehensive matching

3. Apply anon to anonymize the identified patterns

Available entity types:

• anon_nlp_entities(): Anonymize any specified entity types

• anon_nlp_proper_nouns(): Anonymize proper nouns using POS tagging

• anon_nlp_dates(): Anonymize dates

• anon_nlp_dates_and_times(): Anonymize dates and times

• anon_nlp_people(): Anonymize person names

• anon_nlp_organizations(): Anonymize organization names

• anon_nlp_places(): Anonymize place names and locations

• anon_nlp_numbers(): Anonymize numeric entities

• anon_nlp_named(): Anonymize named entities

Global Options

The anon.nlp_default_replacements global option affects the default replacement values used by these functions when no default_replacement argument is explicitly provided. See nlp_default_replacements() to generate the content for the option.

Examples

text <- c("John Smith works at Microsoft in Seattle.",
          "The deal was worth $1.2 million in 2023.",
          "He was the first employee to make 100% of his 3rd quarter targets.")

# Anonymize all entities
anon_nlp_entities(text)
#> [1] "[PERSON] works at [ORG] in [GPE]."                                 
#> [2] "The deal was worth [MONEY] in [DATE]."                             
#> [3] "He was the [ORDINAL] employee to make [PERCENT] of [DATE] targets."

# Anonymize person names
anon_nlp_people(text)
#> [1] "[PERSON] works at Microsoft in Seattle."                           
#> [2] "The deal was worth $1.2 million in 2023."                          
#> [3] "He was the first employee to make 100% of his 3rd quarter targets."

# Anonymize organizations with custom replacement
anon_nlp_organizations(text, default_replacement = "[COMPANY]")
#> [1] "John Smith works at [COMPANY] in Seattle."                         
#> [2] "The deal was worth $1.2 million in 2023."                          
#> [3] "He was the first employee to make 100% of his 3rd quarter targets."

# Anonymize specific entity types
anon_nlp_entities(text, entity_types = c("PERSON", "ORG"))
#> [1] "[PERSON] works at [ORG] in Seattle."                               
#> [2] "The deal was worth $1.2 million in 2023."                          
#> [3] "He was the first employee to make 100% of his 3rd quarter targets."