Content enrichment

Content enrichment

 
 

We can help you make data a more powerful resource by getting more out of it, doing more with it, accessing it more easily. Combining best-of-breed text mining technologies with human domain expertise enables us to know the correct answers and solutions to essential challenges. A team of 400+ data analysts, who speak 40+ languages and boast extensive country, region and industry-specific knowledge, train the technology with your own data set, which allow us to reach 90%-99% high precision and quality of results, achievable usually through manual work.

 

 


Problems

Enormous quantity of data which needs
to be analysed in order for you to identify
and classify specific entities of interest.


Solution: Entity extraction & classification

Pinpointing people, places, companies, events in your content.
Our machine learning models are so sophisticated,
you’ll discover entities you didn’t know about.


 


Problems

Serving relevant content to your audiences.
Updating an outdated taxonomy, but it is expensive
and time-consuming for a human to do it.


Solution: Multi-label tagging

Automatically evaluating a piece of content and tagging it
to a set of categories like topics, industries, countries.
Automatically applying a taxonomy on millions of articles.


 


Problems

Struggling to extract and evaluate in a short time
the mentions of your company, products, events or campaigns.
Getting a quick overview of your brand health.


Solution: Sentiment analysis

Automatically extracting sentiments (positivity/negativity)
and emotions (liking, anger, disgust, etc.)
from unstructured text information.


 


Problems

Looking for ways to automate content recommendations
that will engage visitors to your website.


Solution: Content recommendations

Automatically grouping similar documents together
and generating relevant content recommendations.

 

Case study

A global financial data and news publisher had a request for processing a large set of news articles to extract entities which were reported to have been involved in a specific criminal activity as well as the phase of the legal process and the crime’s monetary value. Our solution was to use semantic technologies based on entity-related context and specific rules for relevancy prediction based on the project criteria. NER and entity classification, both based on machine learning, came in handy – we’ve trained a classifier to identify the entities which the client was interested in.

Drop us a line

Ask us about our services. We’ll be happy to help.