A new way of representing language

Cortical.io has developed a new machine learning methodology inspired by the latest findings on the way the brain processes information. Semantic Folding creates a new data representation, the Semantic Fingerprint, that encodes meaning explicitly, including all senses and contexts. The system “understands” the relatedness of two items by measuring the overlap of their fingerprints. As a result, it is very fast, reliable and easy to implement - a breakthrough technology that leverages the intelligence of the brain to enable the Natural Language Processing of Big Text Data.

How does it work?

To begin with, a semantic space called a Retina Database is created via unsupervised learning of the reference material (contracts, medical textbooks, financial documents, support requests, etc). A Retina Databse can be trained with different text collections to specialize on specific topics or language domain.

In the next step, the Cortical.io Retina engine is used to convert the text repository into semantic fingerprints, a numerical representation that captures the meaning behind natural language.

  • Semantic fingerprints can be generated for language elements like words, sentences and entire documents.
  • Any two pieces of text can be compared, regardless of length or language.
  • Computational operations can be performed on the meaning contained within text data.

While traditional NLP systems are based on word frequency calculations, the Cortical.io Retina Engine uses a substantially finer-grained representation for every word: 16,000 semantic features are captured for every term.

Semantic fingerprints allow direct comparison of the meanings of any two texts, showing thousands of semantic relations.

With Semantic Folding, semantic spaces are stable across languages, enabling direct comparison of text across languages without machine translation.

Semantic fingerprints are encoded in the form of a Sparse Distributed Representation (SDR): a data structure made up of a large number of individual bits, each of which can be turned on or off. The meaning of a fingerprint is determined by the behavior of these bits, with each one contributing a small amount to the overall meaning.

Learn more about semantic folding Learn more about sparse distributed representations

At a glance:

  • New machine learning approach
  • Inspired by the brain
  • Statistics-free
  • No large training data sets required
  • High computational efficiency

Semantic Folding overcomes the limitations of other machine learning approaches

See a comparison

Semantic folding

A new model for intelligent text processing