Person's Names (NER)
Describes Philter's person names filter.
This filter identifies person's names based on natural language processing (NLP) and named-entity recognition (NER) in text. You can train and use your own NLP NER model.
This filter has no required parameters.
Parameter | Description | Default Value |
|---|---|---|
removePunctuation | When set to true, punctuation will be removed prior to analysis. | false |
firstNameFilterStrategies | A list of filter strategies. | None |
enabled | When set to false, the filter will be disabled and not applied | true |
ignored | A list of terms to be ignored by the filter. | None |
The filter may have zero or more filter strategies. When no filter strategy is given the default strategy of
REDACT is used. When multiple filter strategies are given the filter strategies will be applied in as they are listed. See Filter Strategies for details.Strategy | Description |
|---|---|
REDACT | Replace the sensitive text with a placeholder. |
RANDOM_REPLACE | Replace the sensitive text with a similar, random value. |
STATIC_REPLACE | Replace the sensitive text with a given value. |
CRYPTO_REPLACE | Replace the sensitive text with its encrypted value. |
HASH_SHA256_REPLACE | Replace the sensitive text with its SHA256 hash value. |
ABBREVIATE | Replace the sensitive text with the initials of the text. |
Conditional | Description | Operators |
|---|---|---|
TOKEN | Compares the value of the sensitive text. | == , != |
CONTEXT | Compares the filtering context. | == , != |
CONFIDENCE | Compares the confidence in the sensitive text against a threshold value. | < , <=, > , >=, ==, != |
{
"name": "ner-example",
"identifiers": {
"ner": {
"nerFilterStrategies": [
{
"strategy": "REDACT",
"redactionFormat": "{{{REDACTED-%t}}}"
}
]
}
}
}
Last modified 4mo ago