Natural Language Processing (NLP) is the automatic manipulation of natural language, like speech and text, by software. The history of NLP dates back to the 1950s when Alan Turing developed the Turing Test to determine whether or not a computer is truly intelligent. Until the 1980s, most NLP systems were based on complex sets of hand-written rules. However, starting in the late 1980s, there was a revolution in NLP with the introduction of machine learning algorithms for language processing. This was due to both the steady increase in computational power and the gradual lessening of the dominance of Chomskyan theories of linguistics.
The evolution of NLP has included several major milestones:
– 1950s: Natural language processing has its roots in this decade, when Alan Turing developed the Turing Test to determine whether or not a computer is truly intelligent. The test involves automated interpretation and the generation of natural language as criterion of intelligence.
– 1950s-1990s: NLP was largely rules-based, using handcrafted rules to process language.
– Late 1980s: There was a revolution in NLP with the introduction of machine learning algorithms for language processing.
– 2013: The introduction of word2vec by Mikolov et al. introduced a way to represent similarity and relationships between words through the use of word vectors.
– 2017: The paper "Attention is all you need" introduced attention as an independent learning model, which heralded the transformer dominant world in NLP.
Today, transformers are the new cutting-edge in NLP, and they may seem somewhat abstract, but when we look at the past decade of developments in NLP, they begin to make sense. NLP has substantially transformed our daily life, and it continues to evolve.