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 programming languages: It has “variables” (keywords) and “functions” (structure words).
In natural language, keywords – mainly nouns and proper nouns – provide the knowledge, while the logical structure of sentences is provided by words like definite article “the”, conjunction “or”, basic verb “is/are”, possessive verb “has/have” and past tense verbs “was/were” and “had”.
However, deep-learning networks are not hard-wired to process logic. So, this technique is unable to process the logic that is embedded in natural language. And therefore, this technique is unable to grasp the deeper meaning expressed by humans through natural language.
Deep-learning networks are based on pattern recognition. And therefore, they are limited to perform tasks based on pattern recognition.