Editing Symbolic artificial intelligence (section)

=== Natural language processing ===
{{Main|Natural language processing}}

Natural language processing focuses on treating language as data to perform tasks such as identifying topics without necessarily understanding the intended meaning. Natural language understanding, in contrast, constructs a meaning representation and uses that for further processing, such as answering questions.

[[Parsing]], [[Tokenization (lexical analysis)|tokenizing]], [[spell checker|spelling correction]], [[part-of-speech tagging]], [[shallow parsing|noun and verb phrase chunking]] are all aspects of natural language processing long handled by symbolic AI, but since improved by deep learning approaches. In symbolic AI, [[discourse representation theory]] and first-order logic have been used to represent sentence meanings. [[Latent semantic analysis]] (LSA) and [[explicit semantic analysis]] also provided vector representations of documents. In the latter case, vector components are interpretable as concepts named by Wikipedia articles.

New deep learning approaches based on [[Transformer (machine learning model)|Transformer models]] have now eclipsed these earlier symbolic AI approaches and attained state-of-the-art performance in natural language ''processing''. However, Transformer models are opaque and do not yet produce human-interpretable semantic representations for sentences and documents. Instead, they produce task-specific vectors where the meaning of the vector components is opaque.