643 matches
47 actions
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
[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.
v1.0.0Input
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
{
"type": "object",
"properties": {
"personalNameFull": {
"type": "string"
}
},
"required": [
"personalNameFull"
],
"additionalProperties": false
}Output
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10
- 11
- 12
- 13
- 14
- 15
- 16
- 17
- 18
- 19
- 20
- 21
- 22
- 23
- 24
- 25
- 26
- 27
- 28
- 29
- 30
- 31
- 32
- 33
- 34
- 35
- 36
- 37
- 38
- 39
- 40
- 41
- 42
- 43
- 44
- 45
- 46
- 47
- 48
- 49
- 50
- 51
- 52
- 53
- 54
- 55
- 56
- 57
- 58
- 59
{
"type": "object",
"description": "Classified geo names",
"properties": {
"countriesTop": {
"type": "array",
"description": "List countries (top 10)",
"items": {
"type": "string",
"description": "List countries (top 10)"
}
},
"country": {
"type": "string",
"description": "Most likely country "
},
"countryAlt": {
"type": "string",
"description": "Second best alternative : country "
},
"id": {
"type": "string"
},
"name": {
"type": "string",
"description": "The input name."
},
"probabilityAltCalibrated": {
"type": "number",
"format": "double",
"description": "The calibrated probability for country OR countryAlt to have been guessed correctly. -1 = still calibrating. "
},
"probabilityCalibrated": {
"type": "number",
"format": "double",
"description": "The calibrated probability for country to have been guessed correctly. -1 = still calibrating. "
},
"region": {
"type": "string",
"description": "Most likely region (based on country ISO2 code)"
},
"score": {
"type": "number",
"format": "double",
"description": "Higher score is better, but score is not normalized. Use calibratedProbability if available. "
},
"script": {
"type": "string"
},
"subRegion": {
"type": "string",
"description": "Most likely sub region (based on country ISO2 code)"
},
"topRegion": {
"type": "string",
"description": "Most likely top region (based on country ISO2 code)"
}
}
}