Follow project on Twitter
NederlandsEnglish

2.2.1. Deep-learning networks applied to natural language

Deep-learning networks are able to recognize and to produce patterns of a language. But they are unable to grasp the meaning expressed by humans through natural language, because:

Natural language is like algebra and a programming language.

In natural language, knowledge and logic are combined, in the same way as constants and variables are combined with symbols (and functions) of logic in algebra and programming languages.

In natural language, keywords – mainly nouns and proper nouns – provide the knowledge, while words like definite article “the”, conjunction “or”, basic verb “is/are”, possessive verb “has/have” and past tense verbs “was/were” and “had” provide the logical structure.

However, a DNN is not hard-wired to process logic. So, this technique is unable to process algebra and to execute programming languages. And therefore, this technique is also unable to grasp the meaning expressed by humans through natural language.