How Language Resources Help Machines Understand Humans

Discover the current use of voice assistants by consumers and the implications and likely changes in the near future.

One of the biggest drawbacks of using machine learning to build natural language solutions is the staggeringly large amounts of data required to help machines understand humans.

What comes naturally to us, the relationships between words, phrases, sentences, synonyms, lexical entities, concepts etc., must be learned by a machine.  For enterprises that don’t have a significant amount of relative and categorised data readily available, this a costly and time-consuming part of building conversational AI applications.

Teneo overcomes this issue by providing developers with a unique body of data built on billions of real conversations. Known as the Teneo Language Resources, or TLRs for short, these natural language understanding building blocks are crucial in enabling users to quickly build their own natural language applications.

Developed using machine learning techniques by some of the finest minds in computational linguists, the TLRs allow enterprises to teach new conversational applications all the possible language permutations in a matter of moments. The user simply enters a few representative queries, and the TLRs will enable the application to learn all the different ways a user might ask the same exact question.

Because Teneo is available in over 35 languages, the TLRs enable the application to ‘think’ in your native tongue, while delivering the same linguistic sophistication across every other language required.

Download our guide on how we can help machines understand humans, now.

Machines Understand Humans

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