What is Luma7?
It’s a solution based on an algorithm that analyses text content and understands what are the relations in that content and how they link.
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Our customer base range from Governmental institutions to Online Media Publications
There are several advantages for implementing a solution: -A fully Automated link creation system that doesn't depend on human curation selects the best related content (or the latest, or any other parameter) and frees up time for content producers to do what they do best. -Links and connections are always up-to-date to the latest documents created, linking automatically for any content created in the future. (no need to review past content to update links manually) -Connections sorted by relatedness, ensuring that the best content will get the most attention.
For document management applications, it can be very useful where there are a lot of technical jargon to be able to quickly identify what other documents are mostly related with each one of the terms, or if there are very similar terms.
Our Products
Who Luma7 is it for?
Entity Recognition
Luma7 is able to identify and mark in the content more than 20 million Entities (concepts composed by multiple words) and classify them.Text Classification
Our algorithms are able to classify text and to organize by categories. By doing this we are able to identify what's relevant and how to treat that information.Multiple Languages
Luma7 can work with content in multiple languages in the same page, and process them independently to give results that are language relevant.Model Parametrization
Humans write content in many different ways. In order to have better results we first clean and parametrize all text to transform it in a model as precise as possible.Human Interface
Each application has different requirements,Frequent Data Update
We tried to make very high-quality product and so our code is very neat and clean. Whatever anyone could improve and modify the template to your liking.How do we find related connections ?
The algorithm then generates a model space where it can calculate how closely related each word is arithmetically
From that we can estimate not only concept-to-concept relations, but also concept-to-article, paragraph-to-article and article-to-article.
