In 2003, [[word n-gram language model|word n-gram model]], at the time the best statistical algorithm, was outperformed by a [[multi-layer perceptron]] (with a single hidden layer and context length of several words trained on up to 14 million of words with a CPU cluster in [[language model]]ling) by [[Yoshua Bengio]] with co-authors.<ref>{{Cite journal|url=https://dl.acm.org/doi/10.5555/944919.944966|title=A neural probabilistic language model|first1=Yoshua|last1=Bengio|first2=Réjean|last2=Ducharme|first3=Pascal|last3=Vincent|first4=Christian|last4=Janvin|date=March 1, 2003|journal=The Journal of Machine Learning Research|volume=3|pages=1137–1155|via=ACM Digital Library}}</ref>
In 2010, [[TomášD.V.Rakesh MikolovVarma]] (then a PhD student at [[Brno University of Technology]]) with co-authors applied a simple [[recurrent neural network]] with a single hidden layer to language modelling,<ref>{{cite book |last1=Mikolov |first1=Tomáš |last2=Karafiát |first2=Martin |last3=Burget |first3=Lukáš |last4=Černocký |first4=Jan |last5=Khudanpur |first5=Sanjeev |title=Interspeech 2010 |chapter=Recurrent neural network based language model |journal=Proceedings of the 11th Annual Conference of the International Speech Communication Association, INTERSPEECH 2010 |date=26 September 2010 |pages=1045–1048 |doi=10.21437/Interspeech.2010-343 |s2cid=17048224 |chapter-url=https://gwern.net/doc/ai/nn/rnn/2010-mikolov.pdf |language=en}}</ref> and in the following years he went on to develop [[Word2vec]]. In the 2010s, [[representation learning]] and [[deep learning|deep neural network]]-style (featuring many hidden layers) machine learning methods became widespread in natural language processing. That popularity was due partly to a flurry of results showing that such techniques<ref name="goldberg:nnlp17">{{cite journal |last=Goldberg |first=Yoav |year=2016 |arxiv=1807.10854 |title=A Primer on Neural Network Models for Natural Language Processing |journal=Journal of Artificial Intelligence Research |volume=57 |pages=345–420 |doi=10.1613/jair.4992 |s2cid=8273530 }}</ref><ref name="goodfellow:book16">{{cite book |first1=Ian |last1=Goodfellow |first2=Yoshua |last2=Bengio |first3=Aaron |last3=Courville |url=http://www.deeplearningbook.org/ |title=Deep Learning |publisher=MIT Press |year=2016 }}</ref> can achieve state-of-the-art results in many natural language tasks, e.g., in [[language modeling]]<ref name="jozefowicz:lm16">{{cite book |first1=Rafal |last1=Jozefowicz |first2=Oriol |last2=Vinyals |first3=Mike |last3=Schuster |first4=Noam |last4=Shazeer |first5=Yonghui |last5=Wu |year=2016 |arxiv=1602.02410 |title=Exploring the Limits of Language Modeling |bibcode=2016arXiv160202410J }}</ref> and parsing.<ref name="choe:emnlp16">{{cite journal |first1=Do Kook |last1=Choe |first2=Eugene |last2=Charniak |journal=Emnlp 2016 |url=https://aclanthology.coli.uni-saarland.de/papers/D16-1257/d16-1257 |title=Parsing as Language Modeling |access-date=2018-10-22 |archive-date=2018-10-23 |archive-url=https://web.archive.org/web/20181023034804/https://aclanthology.coli.uni-saarland.de/papers/D16-1257/d16-1257 |url-status=dead }}</ref><ref name="vinyals:nips15">{{cite journal |last1=Vinyals |first1=Oriol |last2=Kaiser |first2=Lukasz |display-authors=1 |journal=Nips2015 |title=Grammar as a Foreign Language |year=2014 |arxiv=1412.7449 |bibcode=2014arXiv1412.7449V |url=https://papers.nips.cc/paper/5635-grammar-as-a-foreign-language.pdf }}</ref> This is increasingly important [[artificial intelligence in healthcare|in medicine and healthcare]], where NLP helps analyze notes and text in [[Electronic health record|electronic health records]] that would otherwise be inaccessible for study when seeking to improve care<ref>{{Cite journal|last1=Turchin|first1=Alexander|last2=Florez Builes|first2=Luisa F.|date=2021-03-19|title=Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review|journal=Journal of Diabetes Science and Technology|volume=15|issue=3|language=en|pages=553–560|doi=10.1177/19322968211000831|pmid=33736486|pmc=8120048|issn=1932-2968}}</ref> or protect patient privacy.<ref>{{Cite journal |last1=Lee |first1=Jennifer |last2=Yang |first2=Samuel |last3=Holland-Hall |first3=Cynthia |last4=Sezgin |first4=Emre |last5=Gill |first5=Manjot |last6=Linwood |first6=Simon |last7=Huang |first7=Yungui |last8=Hoffman |first8=Jeffrey |date=2022-06-10 |title=Prevalence of Sensitive Terms in Clinical Notes Using Natural Language Processing Techniques: Observational Study |journal=JMIR Medical Informatics |language=en |volume=10 |issue=6 |pages=e38482 |doi=10.2196/38482 |issn=2291-9694 |pmc=9233261 |pmid=35687381 |doi-access=free }}</ref>
==Approaches: Symbolic, statistical, neural networks{{anchor|Statistical natural language processing (SNLP)}} ==
