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Harald Sack

Building on the 90s, statistical n-gram language models, trained on vast text collections, became the backbone of NLP research. They fueled advancements in nearly all NLP techniques of the era, laying the groundwork for today's AI.

F. Jelinek (1997), Statistical Methods for Speech Recognition, MIT Press, Cambridge, MA

@fizise @fiz_karlsruhe @tabea @enorouzi @sourisnumerique

Slide from Information Service Engineering 2025, LEcture 02, Natural Language PRocessing 01, A Brief History of NLP, NLP timeline. The timeline is located in the middle of the slide from top to bottom. The pointer on the timeline indicates 1990s. On the left, the formula for conditional probability of a word, following a given series of words, is given as a formula. Below, an AI generated portrait of William Shakespeare is displayed with 4 speech buubles, representing artificially generated text based on 1-grams, 2-grams, 3-grams and 4 grams. The 4-grams text example looks a lot like original Shakespeare text. On the right side the following text is displayed: N-grams for statistical language modeling were introduced and popularised by Frederick Jelinek and Stanley F. Chen from IBM Thomas J. Watson Research Center, who developed efficient algorithms and techniques for estimating n-gram probabilities from large text corpora for speech recognition and machine translation. Bibliographical reference: F. Jelinek (1997), Statistical Methods for Speech Recognition, MIT Press, Cambridge, MA.
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