Statistical NLP vs Rules NLP

With its next-generation statistical-based natural language processing (NLP) engines, ICONTEK sets a new standard for ease of use, quality of user experience and speed to impact for operators.

The Death of Rules-Based NLP

Traditional rules-based NLP fails for critical business

But the reality is rules-based NLP systems have severe limitations & are hard to implement. Effectively, all attempts to deploy rules-based NLP for critical business operations have failed due to issues of inaccuracy, poor user experience & prohibitive costs.

  • Insufficient accuracy
  • Poor user experience
  • Prohibitive costs

The many limitations of rules-based NLP

Rules-based NLP systems learn in a manner comparable to how people learn a new language at school – with transcription, an alphabet, spelling and grammar rules. It’s a lot of work and a lengthy process with problems due to numerous layers of translation, data loss and biases.

  • Transcription, dictionaries and grammar rules
  • Numerous layers of translation with data loss and biases
  • High risk of misunderstanding the meaning

Next-Generation Statistical NLP

ICONTEK offers a new approach to NLP that is 100 percent statistical

We believe the most efficient way to learn – for machines and humans – is to make natural associations between stimulus and meaning, as opposed to applying complex human-made rulesets. The approach is to mimic nature as much as possible.

  • 100% statistical-based
  • 100% true machine learning
  • No complex human-made rulesets

ICONTEK’s NLP is self-learning and writes its own rulesets

ICONTEK-powered bots observe human agents and end users, learn at lightning speed, and build their own sophisticated rulesets. Machine-built rulesets can have near unlimited complexity and subtlety, resulting in extraordinary accuracy.

  • Pattern recognition technology is language agnostic
  • Machine-built rulesets deliver breakthrough performance
  • No expensive dictionaries and no expensive consultants

Pattern-based learning instead of rules-based learning

When ICONTEK bots learn by observing agents and end users, it’s comparable to the way children learn their first language at home from family: with direct allocation of meaning to any given stimulus.

For example, when children hear, “Don’t do that!” enough, they recognize a pattern and associate that the speaker’s intent is for them to stop what they’re doing.