Follow project on Twitter

1.5. Fundamental flaw in NLP

The quality of a system is determined by the quality of its output, divided by the quality of its input. The quality of the current approach to NLP is very bad:
• Rich and meaningful sentences in;
• Artificially linked keywords out.

During the NLP process, the logical structure of the sentences is lost, like a two-dimensional movie has lost the three-dimensional spatial information. To prove this loss of the logical structure – and the poor state of the current approach to NLP: You will not find any system – other than Thinknowlogy – able to convert a sentence like “Paul is a son of John” to “John has a son, called Paul” – and vice versa – in a generic way (=through an algorithm).

Both sentences mentioned above have the same meaning. So, it is possible to convert one sentence to the other – and back – through an algorithm. So, why are scientists unable to define such an algorithm?

Only if the involved laws of nature are understood, one is able to convert light to electricity and back, motion to electricity and back, and so on. In the same way, converting one sentence to another – while preserving the quality (=meaning) – requires to understand the Laws of Intelligence that are naturally found in the Human Language. However, not a single scientific paper supports the mentioned conversion in a generic way (=through an algorithm).

In its infancy, Thinknowlogy only accepts a very limited grammar. However, its output has (almost) the same quality as its input, which is a quality ratio of (almost) 100%. It proves: Thinknowlogy preserves the meaning.