text: **Natural language processing**
  Natural language processing (NLP)[50] allows programs to read, write and communicate in human 
  languages such as English. Specific problems include speech recognition, speech synthesis, machine 
  translation, information extraction, information retrieval and question answering.[51] 
  
  Early work, based on Noam Chomsky's generative grammar and semantic networks, had difficulty with 
  word-sense disambiguation[f] unless restricted to small domains called "micro-worlds" (due to the 
  common sense knowledge problem[29]). Margaret Masterman believed that it was meaning and not 
  grammar that was the key to understanding languages, and that thesauri and not dictionaries should be the 
  basis of computational language structure. 
  
  Modern deep learning techniques for NLP include word embedding (representing words, typically as 
  vectors encoding their meaning),[52] transformers (a deep learning architecture using an attention 
  mechanism),[53] and others.[54] In 2019, generative pre-trained transformer (or "GPT") language models 
  began to generate coherent text,[55][56] and by 2023, these models were able to get human-level scores on 
  the bar exam, SAT test, GRE test, and many other real-world applications.[57] 