semantic analysis

Semantic analysis API helps users cluster similar articles by understanding the relatedness between different content and streamlines research by eliminating redundant text contents. Semantic analysis API can help bloggers, publishing and media houses to write more engaging stories by retrieving similar articles from the past quickly, and news aggregators to combine similar news from different sources to reduce clutter in the feeds of their readers.

demo- enter a text
Relatedness
4.09

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 similarity = pd.similarity("Sachin is the greatest batsman","Tendulkar is the finest cricketer");
System.out.println(similarity);
//Response
{
	"actual_score": 0.842932,
	"normalized_score": 4.931469
}
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
<similarity("Sachin is the greatest batsman","Tendulkar is the finest cricketer");
#Response
{
	"actual_score": 0.842932,
	"normalized_score": 4.931469
}
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'
similarity( 'Sachin is the greatest batsman, Tendulkar is the finest cricketer' )
#Response
{
	"actual_score": 0.842932,
	"normalized_score": 4.931469
}
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>");
var similarity = pd.similarity('Sachin is the greatest batsman', 'Tendulkar is the finest cricketer');
Console.WriteLine(similarity);
#Response
{
	"actual_score": 0.842932,
	"normalized_score": 4.931469
}
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');
similarity('Sachin is the greatest batsman', 'Tendulkar is the finest cricketer');
#Response
{
	"actual_score": 0.842932,
	"normalized_score": 4.931469
}

#semantic analysis

function name
paralleldots_similarity
description

Semantic analysis API helps users cluster similar articles by understanding relatedness between different textual content and streamlines research by eliminating redundant text contents. Using semantic analysis (similarity) API function, paralleldots_similarity, you give two text inputs as parameters to compare and get a relatedness score out of 5. A score between 0-2.5 shows low similarity, 2.5 to 3.5 shows mild correlation and a score between 3.5 to 5 shows strong similarity.
Consider the following example where two sentences are being compared “Global warming set to exceed Paris agreement’s 1.5C limit by 2040s, according to draft UN report” and “There is a tipping point’: UN warns climate change goals laid out in Paris accord are almost out of reach”.

example

Using the function paralleldots_sentiment you can analyse any textual content and in return get the sentiment attached to the text.

how our semantic analysis api works?

Semantic similarity API understands relatedness between different pieces of text. It helps in comparing the structure and meaning of the text which can be used to extract similar text and phrases from corpus.

Our API converts textual information to its corresponding document embeddings and the cosine similarity between the two embeddings is scaled to provide the result. The document embeddings are made using Recursive Auto Encoders. These encoders try to reconstruct the given sentences to determine their respective document embeddings.Semantic Similarity API provides a score on a range of 0-5 (0-Not similar, 5-Almost same)

semantic analysis use cases

Semantic analysis API helps users cluster similar articles by understanding the relatedness between different content and streamlines research by eliminating redundant text contents. Semantic analysis API can help bloggers, publishing and media houses to write more engaging stories by retrieving similar articles from the past quickly, and news aggregators to combine similar news from different sources to reduce clutter in the feeds of their readers.

read more
why our semantic analysis 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.