Semantics means the meaning and interpretation of words, signs, and sentence structure. Semantics largely determine our reading comprehension, how we understand others, and even what decisions we make as a result of our interpretations. Semantics can also refer to the branch of study within linguistics that deals with language and how we understand meaning. This has been a particularly interesting field for philosophers as they debate the essence of meaning, how we build meaning, how we share meaning with others, and how meaning changes over time.so actually in artificial intelligence what do semantic analysis used for ?
In Natural Language Processing (NLP), you are dealing with knowledge/intents/... expressed in linguistic structures. For example, if you have a text understanding system, you would read the text, usually sentence by sentence, analyse the syntactic structure, and then the meaning (composed of the word meaning and the structural meaning, plus the pragmatic meaning (based on the situation/context)); in other words, looking at the semantic content of a sentence helps you understand what it is about. An AI system can then process the content towards the goal of understanding the text.
In natural language generation, a semantic representation is turned into text. Here the process in reversed: the meaning of the sentence is known, but the appropriate words and sentence structures have to be chosen by the generator.
These are two examples where semantic analysis is used in NLP; there are plenty of others, as meaning is central to the processing of natural language.
In supervised learning the semantic gets injected into the NN via the supervision signal
For example, a typical pedestrian detection NN trained in a supervised learning way, has no knowledge about what the label "pedestrian" actually means before training, this semantic is injected during training by means of the supervision signal
However this "top-down" approach (based on a high level supervision signal) is not the only way semantic can be got from data because data itself, in its representation space, has a structure (like a manifold in its container space) which can be automatically discovered by NN in an unsupervised learning way like for