NamSor API v2

47 actions

[CREDITS 1 UNIT] Feedback loop to better infer the likely phone prefix, given a personal name and formatted / unformatted phone number, with a local context (ISO2 country of residence).GETv1.0.0
[CREDITS 1 UNIT] Feedback loop to better perform matching Japanese name in KANJI ex. 山本早苗 with a romanized name ex. Yamamoto SanaeGETv1.0.0
[USES 10 UNITS PER NAME] Infer a US resident's likely race/ethnicity according to US Census taxonomy W_NL (white, non latino), HL (hispano latino), A (asian, non latino), B_NL (black, non latino). Optionally add header X-OPTION-USRACEETHNICITY-TAXONOMY: USRACEETHNICITY-6CLASSES for two additional classes, AI_AN (American Indian or Alaskan Native) and PI (Pacific Islander).GETv1.0.0
[USES 10 UNITS PER NAME] Infer a US resident's likely race/ethnicity according to US Census taxonomy, using (optional) ZIP5 code info. Output is W_NL (white, non latino), HL (hispano latino), A (asian, non latino), B_NL (black, non latino). Optionally add header X-OPTION-USRACEETHNICITY-TAXONOMY: USRACEETHNICITY-6CLASSES for two additional classes, AI_AN (American Indian or Alaskan Native) and PI (Pacific Islander).GETv1.0.0
[USES 10 UNITS PER NAME] Infer the likely country of origin of a personal name. Assumes names as they are in the country of origin. For US, CA, AU, NZ and other melting-pots : use 'diaspora' instead.GETv1.0.0
[USES 10 UNITS PER NAME] Infer the likely country of residence of a personal full name, or one surname. Assumes names as they are in the country of residence OR the country of origin.GETv1.0.0
[USES 10 UNITS PER NAME] Infer the likely Indian name castegroup of a personal full name.GETv1.0.0
[USES 10 UNITS PER NAME] Infer the likely Indian state of Union territory according to ISO 3166-2:IN based on the name.GETv1.0.0
[USES 10 UNITS PER NAME] Infer the likely origin of a name at a country subclassification level (state or regeion). Initially, this is only supported for India (ISO2 code 'IN').GETv1.0.0
[USES 10 UNITS PER NAME] Infer the likely religion of a personal Indian full name, provided the Indian state or Union territory (NB/ this can be inferred using the subclassification endpoint).GETv1.0.0
[USES 11 UNITS PER NAME] Infer the likely country and phone prefix, given a personal name and formatted / unformatted phone number.GETv1.0.0
[USES 11 UNITS PER NAME] Infer the likely phone prefix, given a personal name and formatted / unformatted phone number, with a local context (ISO2 country of residence).GETv1.0.0
[USES 20 UNITS PER NAME COUPLE] Infer several classifications for a cross border interaction between names (ex. remit, travel, intl com)GETv1.0.0
[USES 20 UNITS PER NAME] Infer the likely ethnicity/diaspora of a personal name, given a country of residence ISO2 code (ex. US, CA, AU, NZ etc.)GETv1.0.0
Activate/deactivate anonymization for a source.GETv1.0.0
Activate/deactivate learning from a source.GETv1.0.0
Identify Chinese name candidates, based on the romanized name ex. Wang XiaomingGETv1.0.0
Identify Chinese name candidates, based on the romanized name ex. Wang Xiaoming - having a known gender ('male' or 'female')GETv1.0.0
Identify japanese name candidates in KANJI, based on the romanized name ex. Yamamoto SanaeGETv1.0.0
Identify japanese name candidates in KANJI, based on the romanized name ex. Yamamoto Sanae - and a known gender.GETv1.0.0
Infer the likely first/last name structure of a name, ex. John Smith or SMITH, John or SMITH; John. GETv1.0.0
Infer the likely first/last name structure of a name, ex. John Smith or SMITH, John or SMITH; John. For better accuracy, provide a geographic context.GETv1.0.0
Infer the likely first/last name structure of a name, ex. 山本早苗 or Yamamoto SanaeGETv1.0.0
Infer the likely first/last name structure of a name, ex. 王晓明 -> 王(surname) 晓明(given name)GETv1.0.0
Infer the likely gender of a Chinese full name ex. 王晓明GETv1.0.0
Infer the likely gender of a Chinese name in LATIN (Pinyin).GETv1.0.0
Infer the likely gender of a full name, ex. John H. SmithGETv1.0.0
Infer the likely gender of a full name, given a local context (ISO2 country code).GETv1.0.0
Infer the likely gender of a Japanese full name ex. 王晓明GETv1.0.0
Infer the likely gender of a Japanese name in LATIN (Pinyin).GETv1.0.0
Infer the likely gender of a just a fiven name, assuming default 'US' local context. Please use preferably full names and local geographic context for better accuracy.GETv1.0.0
Infer the likely gender of a name, given a local context (ISO2 country code).GETv1.0.0
Infer the likely gender of a name.GETv1.0.0
Infer the likely type of a proper noun (personal name, brand name, place name etc.)GETv1.0.0
Infer the likely type of a proper noun (personal name, brand name, place name etc.)GETv1.0.0
List of classification services and usage cost in Units per classification (default is 1=ONE Unit). Some API endpoints (ex. Corridor) combine multiple classifiers.GETv1.0.0
Print basic source statistics.GETv1.0.0
Print current API usage.GETv1.0.0
Print historical API usage (in an aggregated view, by service, by day/hour/min).GETv1.0.0
Print historical API usage.GETv1.0.0
Print the taxonomy classes valid for the given classifier.GETv1.0.0
Prints the current status of the classifiers. A classifier name in apiStatus corresponds to a service name in apiServices.GETv1.0.0
Read API Key info.GETv1.0.0
Return a score for matching Chinese name ex. 王晓明 with a romanized name ex. Wang XiaomingGETv1.0.0
Return a score for matching Japanese name in KANJI ex. 山本早苗 with a romanized name ex. Yamamoto SanaeGETv1.0.0
Romanize japanese name, based on the name in Kanji.GETv1.0.0
Romanize the Chinese name to Pinyin, ex. 王晓明 -> Wang (surname) Xiaoming (given name)GETv1.0.0

Other/NamSor API v2

Infer the likely type of a proper noun (personal name, brand name, place name etc.)

v1.0.0

Input

{
  "type": "object",
  "properties": {
    "properNoun": {
      "type": "string"
    }
  },
  "required": [
    "properNoun"
  ],
  "additionalProperties": false
}

Output

{
  "type": "object",
  "description": "Classified typed proper names",
  "properties": {
    "commonType": {
      "type": "string",
      "description": "The most likely common name type"
    },
    "commonTypeAlt": {
      "type": "string",
      "description": "Best alternative for : The most likely common name type"
    },
    "id": {
      "type": "string"
    },
    "name": {
      "type": "string",
      "description": "The input name"
    },
    "score": {
      "type": "number",
      "format": "double",
      "description": "Higher score is better, but score is not normalized. Use calibratedProbability if available. "
    },
    "script": {
      "type": "string"
    }
  }
}