Text Analytics API (v3.1-preview.4)
The Text Analytics API is a suite of natural language processing (NLP) services built with best-in-class Microsoft machine learning algorithms. The API can be used to analyze unstructured text using NLP tasks such as sentiment analysis, named entity recognition (NER) of general and personally identifiable information domains, key phrase extraction (KPE) and language detection. It can also be used to batch NER and KPE operations asynchronously to process documents with a single request. Furthermore, this API provides an asynchronous operation for analyzing unstructured clinical and biomedical text using NER, entity linking, relation extraction and entity negation tasks. More documentation can be found in https://docs.microsoft.com/en-us/azure/cognitive-services/text-analytics/overview
Submit analysis job
Submit a collection of text documents for analysis. Specify one or more unique tasks to be executed.
Select the testing console in the region where you created your resource:
Open API testing consoleRequest URL
Request headers
string
Media type of the body sent to the API.
string
Subscription key which provides access to this API. Found in your Cognitive Services accounts.
Request body
Collection of documents to analyze and tasks to execute.
{
"displayName": "Extracting Location & US Region",
"analysisInput": {
"documents": [
{
"id": "1",
"language": "en",
"text": "I had a wonderful trip to Seattle last week."
},
{
"id": "2",
"language": "en",
"text": "I'm flying to NYC tomorrow. See you there."
}
]
},
"tasks": {
"entityRecognitionTasks": [
{
"parameters": {
"model-version": "latest"
}
}
]
}
}{
"allOf": [
{
"properties": {
"displayName": {
"description": "Optional display name for the analysis job.",
"type": "string"
}
},
"type": "object"
},
{
"type": "object",
"required": [
"analysisInput"
],
"properties": {
"analysisInput": {
"type": "object",
"required": [
"documents"
],
"properties": {
"documents": {
"type": "array",
"description": "The set of documents to process as part of this batch.",
"items": {
"type": "object",
"required": [
"id",
"text"
],
"properties": {
"id": {
"type": "string",
"description": "A unique, non-empty document identifier."
},
"text": {
"type": "string",
"description": "The input text to process."
},
"language": {
"type": "string",
"description": "(Optional) This is the 2 letter ISO 639-1 representation of a language. For example, use \"en\" for English; \"es\" for Spanish etc. If not set, use \"en\" for English as default."
}
},
"description": "Contains an input document to be analyzed by the service."
}
}
},
"description": "Contains a set of input documents to be analyzed by the service."
}
}
},
{
"properties": {
"tasks": {
"description": "The set of tasks to execute on the input documents. Cannot specify the same task more than once.",
"properties": {
"entityRecognitionTasks": {
"type": "array",
"items": {
"properties": {
"parameters": {
"properties": {
"model-version": {
"default": "latest",
"type": "string"
},
"stringIndexType": {
"default": "TextElements_v8",
"enum": [
"TextElements_v8",
"UnicodeCodePoint",
"Utf16CodeUnit"
],
"x-ms-enum": {
"name": "StringIndexTypeResponse",
"modelAsString": false,
"values": [
{
"value": "TextElements_v8",
"description": "Returned offset and length values will correspond to TextElements (Graphemes and Grapheme clusters) confirming to the Unicode 8.0.0 standard. Use this option if your application is written in .Net Framework or .Net Core and you will be using StringInfo."
},
{
"value": "UnicodeCodePoint",
"description": "Returned offset and length values will correspond to Unicode code points. Use this option if your application is written in a language that support Unicode, for example Python."
},
{
"value": "Utf16CodeUnit",
"description": "Returned offset and length values will correspond to UTF-16 code units. Use this option if your application is written in a language that support Unicode, for example Java, JavaScript."
}
]
},
"type": "string"
}
},
"type": "object"
}
},
"type": "object"
}
},
"entityRecognitionPiiTasks": {
"type": "array",
"items": {
"properties": {
"parameters": {
"properties": {
"domain": {
"default": "none",
"enum": [
"phi",
"none"
],
"type": "string"
},
"model-version": {
"default": "latest",
"type": "string"
},
"piiCategories": {
"$ref": "#/parameters/PiiCategories"
},
"stringIndexType": {
"default": "TextElements_v8",
"enum": [
"TextElements_v8",
"UnicodeCodePoint",
"Utf16CodeUnit"
],
"x-ms-enum": {
"name": "StringIndexTypeResponse",
"modelAsString": false,
"values": [
{
"value": "TextElements_v8",
"description": "Returned offset and length values will correspond to TextElements (Graphemes and Grapheme clusters) confirming to the Unicode 8.0.0 standard. Use this option if your application is written in .Net Framework or .Net Core and you will be using StringInfo."
},
{
"value": "UnicodeCodePoint",
"description": "Returned offset and length values will correspond to Unicode code points. Use this option if your application is written in a language that support Unicode, for example Python."
},
{
"value": "Utf16CodeUnit",
"description": "Returned offset and length values will correspond to UTF-16 code units. Use this option if your application is written in a language that support Unicode, for example Java, JavaScript."
}
]
},
"type": "string"
}
},
"type": "object"
}
},
"type": "object"
}
},
"keyPhraseExtractionTasks": {
"type": "array",
"items": {
"properties": {
"parameters": {
"properties": {
"model-version": {
"default": "latest",
"type": "string"
}
},
"type": "object"
}
},
"type": "object"
}
},
"EntityLinkingTasks": {
"type": "array",
"items": {
"properties": {
"parameters": {
"properties": {
"model-version": {
"default": "latest",
"type": "string"
},
"stringIndexType": {
"default": "TextElements_v8",
"enum": [
"TextElements_v8",
"UnicodeCodePoint",
"Utf16CodeUnit"
],
"x-ms-enum": {
"name": "StringIndexTypeResponse",
"modelAsString": false,
"values": [
{
"value": "TextElements_v8",
"description": "Returned offset and length values will correspond to TextElements (Graphemes and Grapheme clusters) confirming to the Unicode 8.0.0 standard. Use this option if your application is written in .Net Framework or .Net Core and you will be using StringInfo."
},
{
"value": "UnicodeCodePoint",
"description": "Returned offset and length values will correspond to Unicode code points. Use this option if your application is written in a language that support Unicode, for example Python."
},
{
"value": "Utf16CodeUnit",
"description": "Returned offset and length values will correspond to UTF-16 code units. Use this option if your application is written in a language that support Unicode, for example Java, JavaScript."
}
]
},
"type": "string"
}
},
"type": "object"
}
},
"type": "object"
}
}
},
"type": "object"
}
},
"required": [
"tasks"
],
"type": "object"
}
]
}{
"displayName": "Extracting Location & US Region",
"analysisInput": {
"documents": [
{
"id": "1",
"language": "en",
"text": "I had a wonderful trip to Seattle last week."
},
{
"id": "2",
"language": "en",
"text": "I'm flying to NYC tomorrow. See you there."
}
]
},
"tasks": {
"entityRecognitionTasks": [
{
"parameters": {
"model-version": "latest"
}
}
]
}
}{
"allOf": [
{
"properties": {
"displayName": {
"description": "Optional display name for the analysis job.",
"type": "string"
}
},
"type": "object"
},
{
"type": "object",
"required": [
"analysisInput"
],
"properties": {
"analysisInput": {
"type": "object",
"required": [
"documents"
],
"properties": {
"documents": {
"type": "array",
"description": "The set of documents to process as part of this batch.",
"items": {
"type": "object",
"required": [
"id",
"text"
],
"properties": {
"id": {
"type": "string",
"description": "A unique, non-empty document identifier."
},
"text": {
"type": "string",
"description": "The input text to process."
},
"language": {
"type": "string",
"description": "(Optional) This is the 2 letter ISO 639-1 representation of a language. For example, use \"en\" for English; \"es\" for Spanish etc. If not set, use \"en\" for English as default."
}
},
"description": "Contains an input document to be analyzed by the service."
}
}
},
"description": "Contains a set of input documents to be analyzed by the service."
}
}
},
{
"properties": {
"tasks": {
"description": "The set of tasks to execute on the input documents. Cannot specify the same task more than once.",
"properties": {
"entityRecognitionTasks": {
"type": "array",
"items": {
"properties": {
"parameters": {
"properties": {
"model-version": {
"default": "latest",
"type": "string"
},
"stringIndexType": {
"default": "TextElements_v8",
"enum": [
"TextElements_v8",
"UnicodeCodePoint",
"Utf16CodeUnit"
],
"x-ms-enum": {
"name": "StringIndexTypeResponse",
"modelAsString": false,
"values": [
{
"value": "TextElements_v8",
"description": "Returned offset and length values will correspond to TextElements (Graphemes and Grapheme clusters) confirming to the Unicode 8.0.0 standard. Use this option if your application is written in .Net Framework or .Net Core and you will be using StringInfo."
},
{
"value": "UnicodeCodePoint",
"description": "Returned offset and length values will correspond to Unicode code points. Use this option if your application is written in a language that support Unicode, for example Python."
},
{
"value": "Utf16CodeUnit",
"description": "Returned offset and length values will correspond to UTF-16 code units. Use this option if your application is written in a language that support Unicode, for example Java, JavaScript."
}
]
},
"type": "string"
}
},
"type": "object"
}
},
"type": "object"
}
},
"entityRecognitionPiiTasks": {
"type": "array",
"items": {
"properties": {
"parameters": {
"properties": {
"domain": {
"default": "none",
"enum": [
"phi",
"none"
],
"type": "string"
},
"model-version": {
"default": "latest",
"type": "string"
},
"piiCategories": {
"$ref": "#/parameters/PiiCategories"
},
"stringIndexType": {
"default": "TextElements_v8",
"enum": [
"TextElements_v8",
"UnicodeCodePoint",
"Utf16CodeUnit"
],
"x-ms-enum": {
"name": "StringIndexTypeResponse",
"modelAsString": false,
"values": [
{
"value": "TextElements_v8",
"description": "Returned offset and length values will correspond to TextElements (Graphemes and Grapheme clusters) confirming to the Unicode 8.0.0 standard. Use this option if your application is written in .Net Framework or .Net Core and you will be using StringInfo."
},
{
"value": "UnicodeCodePoint",
"description": "Returned offset and length values will correspond to Unicode code points. Use this option if your application is written in a language that support Unicode, for example Python."
},
{
"value": "Utf16CodeUnit",
"description": "Returned offset and length values will correspond to UTF-16 code units. Use this option if your application is written in a language that support Unicode, for example Java, JavaScript."
}
]
},
"type": "string"
}
},
"type": "object"
}
},
"type": "object"
}
},
"keyPhraseExtractionTasks": {
"type": "array",
"items": {
"properties": {
"parameters": {
"properties": {
"model-version": {
"default": "latest",
"type": "string"
}
},
"type": "object"
}
},
"type": "object"
}
},
"EntityLinkingTasks": {
"type": "array",
"items": {
"properties": {
"parameters": {
"properties": {
"model-version": {
"default": "latest",
"type": "string"
},
"stringIndexType": {
"default": "TextElements_v8",
"enum": [
"TextElements_v8",
"UnicodeCodePoint",
"Utf16CodeUnit"
],
"x-ms-enum": {
"name": "StringIndexTypeResponse",
"modelAsString": false,
"values": [
{
"value": "TextElements_v8",
"description": "Returned offset and length values will correspond to TextElements (Graphemes and Grapheme clusters) confirming to the Unicode 8.0.0 standard. Use this option if your application is written in .Net Framework or .Net Core and you will be using StringInfo."
},
{
"value": "UnicodeCodePoint",
"description": "Returned offset and length values will correspond to Unicode code points. Use this option if your application is written in a language that support Unicode, for example Python."
},
{
"value": "Utf16CodeUnit",
"description": "Returned offset and length values will correspond to UTF-16 code units. Use this option if your application is written in a language that support Unicode, for example Java, JavaScript."
}
]
},
"type": "string"
}
},
"type": "object"
}
},
"type": "object"
}
}
},
"type": "object"
}
},
"required": [
"tasks"
],
"type": "object"
}
]
}Response 202
A successful call results with an Operation-Location header used to check the status of the analysis job.
Response 400
Bad Request.
{
"error": {
"code": "InvalidRequest",
"message": "Invalid Request.",
"innererror": {
"code": "MissingInputRecords",
"message": "Missing input records."
}
}
}{
"error": {
"code": "InvalidRequest",
"message": "Invalid Request.",
"innererror": {
"code": "MissingInputRecords",
"message": "Missing input records."
}
}
}Response 500
Internal error response
{
"error": {
"code": "InternalServerError",
"message": "Processing failed unexpectedly. Please try again later."
}
}{
"error": {
"code": "InternalServerError",
"message": "Processing failed unexpectedly. Please try again later."
}
}Code samples
@ECHO OFF
curl -v -X POST "https://*.cognitiveservices.azure.com/text/analytics/v3.1-preview.4/analyze"
-H "Content-Type: application/json"
-H "Ocp-Apim-Subscription-Key: {subscription key}"
--data-ascii "{body}"
using System;
using System.Net.Http.Headers;
using System.Text;
using System.Net.Http;
using System.Web;
namespace CSHttpClientSample
{
static class Program
{
static void Main()
{
MakeRequest();
Console.WriteLine("Hit ENTER to exit...");
Console.ReadLine();
}
static async void MakeRequest()
{
var client = new HttpClient();
var queryString = HttpUtility.ParseQueryString(string.Empty);
// Request headers
client.DefaultRequestHeaders.Add("Ocp-Apim-Subscription-Key", "{subscription key}");
var uri = "https://*.cognitiveservices.azure.com/text/analytics/v3.1-preview.4/analyze?" + queryString;
HttpResponseMessage response;
// Request body
byte[] byteData = Encoding.UTF8.GetBytes("{body}");
using (var content = new ByteArrayContent(byteData))
{
content.Headers.ContentType = new MediaTypeHeaderValue("< your content type, i.e. application/json >");
response = await client.PostAsync(uri, content);
}
}
}
} // // This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
import java.net.URI;
import org.apache.http.HttpEntity;
import org.apache.http.HttpResponse;
import org.apache.http.client.HttpClient;
import org.apache.http.client.methods.HttpGet;
import org.apache.http.client.utils.URIBuilder;
import org.apache.http.impl.client.HttpClients;
import org.apache.http.util.EntityUtils;
public class JavaSample
{
public static void main(String[] args)
{
HttpClient httpclient = HttpClients.createDefault();
try
{
URIBuilder builder = new URIBuilder("https://*.cognitiveservices.azure.com/text/analytics/v3.1-preview.4/analyze");
URI uri = builder.build();
HttpPost request = new HttpPost(uri);
request.setHeader("Content-Type", "application/json");
request.setHeader("Ocp-Apim-Subscription-Key", "{subscription key}");
// Request body
StringEntity reqEntity = new StringEntity("{body}");
request.setEntity(reqEntity);
HttpResponse response = httpclient.execute(request);
HttpEntity entity = response.getEntity();
if (entity != null)
{
System.out.println(EntityUtils.toString(entity));
}
}
catch (Exception e)
{
System.out.println(e.getMessage());
}
}
}
<!DOCTYPE html>
<html>
<head>
<title>JSSample</title>
<script src="http://ajax.googleapis.com/ajax/libs/jquery/1.9.0/jquery.min.js"></script>
</head>
<body>
<script type="text/javascript">
$(function() {
var params = {
// Request parameters
};
$.ajax({
url: "https://*.cognitiveservices.azure.com/text/analytics/v3.1-preview.4/analyze?" + $.param(params),
beforeSend: function(xhrObj){
// Request headers
xhrObj.setRequestHeader("Content-Type","application/json");
xhrObj.setRequestHeader("Ocp-Apim-Subscription-Key","{subscription key}");
},
type: "POST",
// Request body
data: "{body}",
})
.done(function(data) {
alert("success");
})
.fail(function() {
alert("error");
});
});
</script>
</body>
</html>
#import <Foundation/Foundation.h>
int main(int argc, const char * argv[])
{
NSAutoreleasePool * pool = [[NSAutoreleasePool alloc] init];
NSString* path = @"https://*.cognitiveservices.azure.com/text/analytics/v3.1-preview.4/analyze";
NSArray* array = @[
// Request parameters
@"entities=true",
];
NSString* string = [array componentsJoinedByString:@"&"];
path = [path stringByAppendingFormat:@"?%@", string];
NSLog(@"%@", path);
NSMutableURLRequest* _request = [NSMutableURLRequest requestWithURL:[NSURL URLWithString:path]];
[_request setHTTPMethod:@"POST"];
// Request headers
[_request setValue:@"application/json" forHTTPHeaderField:@"Content-Type"];
[_request setValue:@"{subscription key}" forHTTPHeaderField:@"Ocp-Apim-Subscription-Key"];
// Request body
[_request setHTTPBody:[@"{body}" dataUsingEncoding:NSUTF8StringEncoding]];
NSURLResponse *response = nil;
NSError *error = nil;
NSData* _connectionData = [NSURLConnection sendSynchronousRequest:_request returningResponse:&response error:&error];
if (nil != error)
{
NSLog(@"Error: %@", error);
}
else
{
NSError* error = nil;
NSMutableDictionary* json = nil;
NSString* dataString = [[NSString alloc] initWithData:_connectionData encoding:NSUTF8StringEncoding];
NSLog(@"%@", dataString);
if (nil != _connectionData)
{
json = [NSJSONSerialization JSONObjectWithData:_connectionData options:NSJSONReadingMutableContainers error:&error];
}
if (error || !json)
{
NSLog(@"Could not parse loaded json with error:%@", error);
}
NSLog(@"%@", json);
_connectionData = nil;
}
[pool drain];
return 0;
}
<?php
// This sample uses the Apache HTTP client from HTTP Components (http://hc.apache.org/httpcomponents-client-ga/)
require_once 'HTTP/Request2.php';
$request = new Http_Request2('https://*.cognitiveservices.azure.com/text/analytics/v3.1-preview.4/analyze');
$url = $request->getUrl();
$headers = array(
// Request headers
'Content-Type' => 'application/json',
'Ocp-Apim-Subscription-Key' => '{subscription key}',
);
$request->setHeader($headers);
$parameters = array(
// Request parameters
);
$url->setQueryVariables($parameters);
$request->setMethod(HTTP_Request2::METHOD_POST);
// Request body
$request->setBody("{body}");
try
{
$response = $request->send();
echo $response->getBody();
}
catch (HttpException $ex)
{
echo $ex;
}
?>########### Python 2.7 #############
import httplib, urllib, base64
headers = {
# Request headers
'Content-Type': 'application/json',
'Ocp-Apim-Subscription-Key': '{subscription key}',
}
params = urllib.urlencode({
})
try:
conn = httplib.HTTPSConnection('*.cognitiveservices.azure.com')
conn.request("POST", "/text/analytics/v3.1-preview.4/analyze?%s" % params, "{body}", headers)
response = conn.getresponse()
data = response.read()
print(data)
conn.close()
except Exception as e:
print("[Errno {0}] {1}".format(e.errno, e.strerror))
####################################
########### Python 3.2 #############
import http.client, urllib.request, urllib.parse, urllib.error, base64
headers = {
# Request headers
'Content-Type': 'application/json',
'Ocp-Apim-Subscription-Key': '{subscription key}',
}
params = urllib.parse.urlencode({
})
try:
conn = http.client.HTTPSConnection('*.cognitiveservices.azure.com')
conn.request("POST", "/text/analytics/v3.1-preview.4/analyze?%s" % params, "{body}", headers)
response = conn.getresponse()
data = response.read()
print(data)
conn.close()
except Exception as e:
print("[Errno {0}] {1}".format(e.errno, e.strerror))
####################################require 'net/http'
uri = URI('https://*.cognitiveservices.azure.com/text/analytics/v3.1-preview.4/analyze')
uri.query = URI.encode_www_form({
})
request = Net::HTTP::Post.new(uri.request_uri)
# Request headers
request['Content-Type'] = 'application/json'
# Request headers
request['Ocp-Apim-Subscription-Key'] = '{subscription key}'
# Request body
request.body = "{body}"
response = Net::HTTP.start(uri.host, uri.port, :use_ssl => uri.scheme == 'https') do |http|
http.request(request)
end
puts response.body