Named Entity Recognition

Named Entity Recognition can identify individuals, companies, places, organization, cities and other various types of entities. The Named Entity Recognition API can extract this information from any type of text, webpage or social media network.

demo- Enter a text
Named Entities

Ready to Integrate? Check out the API wrappers below

For setup and installation instruction, please visit our Github Page
import paralleldots.ParallelDots;
// Get your API key here
ParallelDots pd = new ParallelDots("<YOUR_API_KEY>");
import paralleldots.ParallelDots;
ParallelDots pd = new ParallelDots();
String ner = pd.ner("Narendra Modi is the prime minister of India");
System.out.println(ner);
//Response
{
	"entities": [
		{
			"category": "place",
			"name": "India",
			"confidence_score": 1.0
		}, 
		{
			"category": "person",
			"name": "Narendra Modi",
			"confidence_score": 1.0
		}
	]
}
For setup and installation instruction, please visit our Github Page
from paralleldots import set_api_key, get_api_key
# Get your API key here
set_api_key(<YOUR_API_KEY>)
get_api_key()
from paralleldots import similarity, ner, taxonomy, sentiment, keywords, intent, emotion, multilang, abuse
ner( "Narendra Modi is the prime minister of India" )
#Response
{
	"entities": [
		{
			"category": "place",
			"name": "India",
			"confidence_score": 1.0
		}, 
		{
			"category": "person",
			"name": "Narendra Modi",
			"confidence_score": 1.0
		}
	]
}
For setup and installation instruction, please visit our Github Page
require 'paralleldots'
# Get your API key here
set_api_key(<YOUR_API_KEY>)
get_api_key()
require 'paralleldots'
ner( "Narendra Modi is the prime minister of India" )
#Response
{
	"entities": [
		{
			"category": "place",
			"name": "India",
			"confidence_score": 1.0
		}, 
		{
			"category": "person",
			"name": "Narendra Modi",
			"confidence_score": 1.0
		}
	]
}
For setup and installation instruction, please visit our Github Page
using ParallelDots
# Get your API key here
ParallelDots.api pd = new ParallelDots.api("<YOUR_API_KEY>");
ner( "Narendra Modi is the prime minister of India" )
Console.WriteLine(ner);
#Response
{
	"entities": [
		{
			"category": "place",
			"name": "India",
			"confidence_score": 1.0
		}, 
		{
			"category": "person",
			"name": "Narendra Modi",
			"confidence_score": 1.0
		}
	]
}
For setup and installation instruction, please visit our Github Page
require(__DIR__ . '/vendor/paralleldots/apis/autoload.php');
# Get your API key here
set_api_key("<YOUR_API_KEY>");
get_api_key();
require(__DIR__ . '/vendor/paralleldots/apis/autoload.php');
ner( "Narendra Modi is the prime minister of India" )
#Response
{
	"entities": [
		{
			"category": "place",
			"name": "India",
			"confidence_score": 1.0
		}, 
		{
			"category": "person",
			"name": "Narendra Modi",
			"confidence_score": 1.0
		}
	]
}
Benchmark

We have benchmarked it on CoNLL 2003 test dataset. Please know that it has not been trained on CoNLL 2003 dataset so this is out of sample data for the algorithm. You can find baselines and other benchmarks here.

Our benchmark results: Precision 0.9, Recall: 0.92, F1 Score: 0.90 (on English News testa and testb).

How our Named Entity Recognition api works?

Named Entity Recognition API seeks to locate and classify elements in text into definitive categories such as names of persons, organizations, locations. It can extract this information in any type of text, be it a web page, piece of news or social media content.

The API uses Deep Learning technology to determine representations of character groupings.The text to be analyzed is broken into word groups and words are further broken down to character groups and neural network trains on both of these granularities. The hypothesis behind the algorithm is that there are two important aspects which determine if a word is a proper noun, the first is the composition of a word, what syllables it uses and what sounds it comprises of and the second is the adjacent words to the considered word.

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Named Entity Recognition use cases

Text classification can come in handy whenever there is a need of organizing large corpus. Platforms such as E-commerce, news agencies, content curators, blogs, directories, government, academia, and likes can use automated technologies to classify and tag content and products. Text classification can be customized heavily to aid highly selective situations involving analyzing and classifying texts.

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why our Named Entity Recognition api ?
Accurate

Highly accurate classification of unstructured textual data.

Real Time

State of the art technology to provide accurate results real-time.

Customizable

Can be trained on custom dataset to obtain similar accuracy and performance.