Essential Techniques and Criteria for Successful Hybrid NL Use Cases

The volume of language data enterprise organizations manage on a daily basis continues to grow at an exponential rate. Those organizations that process and understand their data most effectively will quickly find themselves with an invaluable competitive advantage. This data challenge (and opportunity) has put the spotlight on NLP/NLU technologies and the capabilities they can offer the enterprise.

Where can organizations most benefit from NLP/NLU? Which AI approach is best suited to carry out specific tasks? The answers to these questions are not always as straightforward as you would hope. However, they are critical to making the right decisions for your business. So before you dive head-first into the world of natural language solutions, give yourself a proper education on the things that really matter.

In the following white paper, we break down:

  • The core differences between machine learning and symbolic AI
  • Three common NLP/NLU use cases you should adopt for your business
  • The 12 core criteria you should evaluate before selecting your AI approach