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* '''1980s''': The 1980s and early 1990s mark the heyday of symbolic methods in NLP. Focus areas of the time included research on rule-based parsing (e.g., the development of [[Head-driven phrase structure grammar|HPSG]] as a computational operationalization of [[generative grammar]]), morphology (e.g., two-level morphology<ref>{{citation|last=Koskenniemi|first=Kimmo|title=Two-level morphology: A general computational model of word-form recognition and production|url=http://www.ling.helsinki.fi/~koskenni/doc/Two-LevelMorphology.pdf|year=1983|publisher=Department of General Linguistics, [[University of Helsinki]]|author-link=Kimmo Koskenniemi}}</ref>), semantics (e.g., [[Lesk algorithm]]), reference (e.g., within Centering Theory<ref>Joshi, A. K., & Weinstein, S. (1981, August). [https://www.ijcai.org/Proceedings/81-1/Papers/071.pdf Control of Inference: Role of Some Aspects of Discourse Structure-Centering]. In ''IJCAI'' (pp. 385–387).</ref>) and other areas of natural language understanding (e.g., in the [[Rhetorical structure theory|Rhetorical Structure Theory]]). Other lines of research were continued, e.g., the development of chatterbots with [[Racter]] and [[Jabberwacky]]. An important development (that eventually led to the statistical turn in the 1990s) was the rising importance of quantitative evaluation in this period.<ref>{{Cite journal|last1=Guida|first1=G.|last2=Mauri|first2=G.|date=July 1986|title=Evaluation of natural language processing systems: Issues and approaches|journal=Proceedings of the IEEE|volume=74|issue=7|pages=1026–1035|doi=10.1109/PROC.1986.13580|s2cid=30688575|issn=1558-2256}}</ref>
=== Statistical NLP (
Up until the 1980s, most natural language processing systems were based on complex sets of hand-written rules. Starting in the late 1980s, however, there was a revolution in natural language processing with the introduction of [[machine learning]] algorithms for language processing. This was due to both the steady increase in computational power (see [[Moore's law]]) and the gradual lessening of the dominance of [[Noam Chomsky|Chomskyan]] theories of linguistics (e.g. [[transformational grammar]]), whose theoretical underpinnings discouraged the sort of [[corpus linguistics]] that underlies the machine-learning approach to language processing.<ref>Chomskyan linguistics encourages the investigation of "[[corner case]]s" that stress the limits of its theoretical models (comparable to [[pathological (mathematics)|pathological]] phenomena in mathematics), typically created using [[thought experiment]]s, rather than the systematic investigation of typical phenomena that occur in real-world data, as is the case in [[corpus linguistics]]. The creation and use of such [[text corpus|corpora]] of real-world data is a fundamental part of machine-learning algorithms for natural language processing. In addition, theoretical underpinnings of Chomskyan linguistics such as the so-called "[[poverty of the stimulus]]" argument entail that general learning algorithms, as are typically used in machine learning, cannot be successful in language processing. As a result, the Chomskyan paradigm discouraged the application of such models to language processing.</ref>
*'''1990s''': Many of the notable early successes in statistical methods in NLP occurred in the field of [[machine translation]], due especially to work at IBM Research, such as [[IBM alignment models]]. These systems were able to take advantage of existing multilingual [[text corpus|textual corpora]] that had been produced by the [[Parliament of Canada]] and the [[European Union]] as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government. However, most other systems depended on corpora specifically developed for the tasks implemented by these systems, which was (and often continues to be) a major limitation in the success of these systems. As a result, a great deal of research has gone into methods of more effectively learning from limited amounts of data.
*'''2000s''': With the growth of the web, increasing amounts of raw (unannotated) language data have become available since the mid-1990s. Research has thus increasingly focused on [[unsupervised learning|unsupervised]] and [[semi-supervised learning]] algorithms. Such algorithms can learn from data that has not been hand-annotated with the desired answers or using a combination of annotated and non-annotated data. Generally, this task is much more difficult than [[supervised learning]], and typically produces less accurate results for a given amount of input data. However, there is an enormous amount of non-annotated data available (including, among other things, the entire content of the [[World Wide Web]]), which can often make up for the
▲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, [[Tomáš Mikolov]] (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)}} ==
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