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Natural Language Processing Techniques

Natural Language Processing Techniques is a research topic within Artificial Intelligence. Science Explorer counts 89k research works in it since 1950. 9.7% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on statistical machine translation, neural machine translation, dependency parsing, word sense disambiguation, and part-of-speech tagging. It also covers topics such as corpus linguistics, syntax-based translation models, multilingual neural machine translation, and language modeling.

  • Statistical Machine Translation
  • Neural Machine Translation
  • Dependency Parsing
  • Word Sense Disambiguation
  • Part-of-Speech Tagging
  • Corpus Linguistics
  • Syntax-based Translation Models
  • Multilingual Neural Machine Translation
  • Lexical Database
  • Language Modeling
Research works
89k
fractional, since 1950
In the world top 10%
8.7k
per year above
Top-10% rate
9.7%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+33%
the tick is no change
Largest producer
China
37% of the 10 largest, 2022-now
Natural Language Processing Techniques

Which countries lead Natural Language Processing Techniques research?

By volume, China and the United States publish the most (5.6k and 3.2k works in 2022-now).

By volume, 2022-now

  1. 1 China 5.6k works
  2. 2 United States 3.2k works
  3. 3 India 1.8k works
  4. 4 Germany 914 works
  5. 5 United Kingdom 767 works
  6. 6 France 752 works
  7. 7 Japan 748 works
  8. 8 Canada 447 works
  9. 9 South Korea 435 works
  10. 10 Italy 419 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 37.3%United States: 21.3%India: 11.8%Germany: 6.0%6 others listed: 23.6%37%largest
China5,637 · 37.3%United States3,222 · 21.3%India1,788 · 11.8%Germany914 · 6.0%6 others listed3,570 · 23.6%

Shares of the rows listed above, not of the whole node.

Which institutions lead Natural Language Processing Techniques research?

By volume in 2022-now, Centre National de la Recherche Scientifique publishes the most Natural Language Processing Techniques research, followed by Chinese Academy of Sciences and Microsoft (United States).

By volume, 2022-now

  1. 1 Centre National de la Recherche ScientifiqueFrance 277 works
  2. 2 Chinese Academy of SciencesChina 192 works
  3. 3 Microsoft (United States)United States 135 works
  4. 4 Peking UniversityChina 130 works
  5. 5 Tsinghua UniversityChina 120 works
  6. 6 Shanghai Jiao Tong UniversityChina 109 works
  7. 7 Carnegie Mellon UniversityUnited States 102 works
  8. 8 Alphabet (United States)United States 98 works
  9. 9 Beijing University of Posts and TelecommunicationsChina 98 works
  10. 10 Harbin Institute of TechnologyChina 92 works

Who are the leading researchers in Natural Language Processing Techniques?

The most-cited researchers publishing on Natural Language Processing Techniques include Yoshua Bengio, Geoffrey E. Hinton and Richard M. Durbin.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Geoffrey E. Hinton Canada 454k citations
  3. 3 Richard M. Durbin United Kingdom 410k citations
  4. 4 Peer Bork Germany 396k citations
  5. 5 Steven L. Salzberg United States 372k citations
  6. 6 Xiangyu Zhang 327k citations
  7. 7 Dan J. Stein South Africa 306k citations
  8. 8 Xiaogang Wang China 260k citations
  9. 9 Yann LeCun United States 256k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Natural Language Processing Techniques?

The most cited paper in Natural Language Processing Techniques is "Exploiting Generative AI to Scale up Intelligent Tutoring Systems" (Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra et al., 2023), with 79,061 citations. Next come "AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale" (46,030) and "Glove: Global Vectors for Word Representation" (34,197).

Most cited papers
#PaperReleasedCitations
1 Exploiting Generative AI to Scale up Intelligent Tutoring Systems Jakubův, Jan, Chvalovský, Karel, Goertzel, Zarathustra et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) Open access 2023 79,061
2 AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale Kefallinos, Dionysios, Alexandris, Georgios, Maras, Alexis et al. · arXiv (Cornell University) PreprintOpen access 11 Oct 2018 46,030
3 Glove: Global Vectors for Word Representation Jeffrey Pennington, Richard Socher, Christopher D. Manning Conference paper 2014 34,197
4 Latent dirichlet allocation David M. Blei, Andrew Y. Ng, Michael I. Jordan · Journal of Machine Learning Research Mar 2003 27,026
5 Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation Kyunghyun Cho, Bart van Merriënboer, Çağlar Gülçehre et al. Conference paperOpen access 2014 25,290
All 96 most cited papers, with filters and sorting

Where is Natural Language Processing Techniques research done?

The largest centres of Natural Language Processing Techniques research in 2022-now are Beijing (China), Shanghai (China), Tokyo (Japan) and Seoul (South Korea). Among places with at least 20 works in it, it is an unusually large share of all research in Redmond, San Mateo and Ikoma.

Largest cities, 2022-now

  1. 1 BeijingChina 1.3k works
  2. 2 ShanghaiChina 443 works
  3. 3 TokyoJapan 338 works
  4. 4 SeoulSouth Korea 249 works
  5. 5 GuangzhouChina 242 works
  6. 6 HangzhouChina 232 works
  7. 7 ShenzhenChina 231 works
  8. 8 ParisFrance 231 works
  9. 9 Hong KongChina 212 works
  10. 10 NanjingChina 207 works

Where it is the local speciality

  1. RedmondUS · 44.1 works21×
  2. San MateoUS · 23.6 works19×
  3. IkomaJP · 30.0 works18×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Natural Language Processing Techniques than the world average.

See Natural Language Processing Techniques on the map

Where is the best place to study Natural Language Processing Techniques?

Among universities, judged by research, Nanyang Technological University, Stanford University and Tsinghua University score highest, combining citation excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%30%mean 23.45%fractional works in this field (log) →share of work in the world top 10% →Nanyang Technologica…Tsinghua UniversityStanford UniversityNational University …
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Citation excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Nanyang Technological UniversitySingapore 77.827.8%3.8×58 +54.5%
2Stanford UniversityUnited States 77.723.6%2.2×61 +13.5%
3Tsinghua UniversityChina 75.923.9%2.8×120 +34.6%
4National University of SingaporeSingapore 75.422.3%2.7×61 +71.4%
5Hong Kong University of Science and TechnologyChina 72.120.9%6.1×48 +66.6%
6University of Hong KongChina 69.924.8%2.3×40 +105.9%
7Singapore Management UniversitySingapore 69.323.5%10.9×22 +138.3%
8Carnegie Mellon UniversityUnited States 69.121.8%12.9×102 -37.0%
9University of Illinois Urbana-ChampaignUnited States 69.018.6%4.0×66 +17.9%
10University of WashingtonUnited States 68.727.3%2.3×49 -0.9%

Universities only. The score blends citation excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Natural Language Processing Techniques, country by country

Is Natural Language Processing Techniques research growing?

Output in 2022-2025 was 33% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Natural Language Processing Techniques.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Natural Language Processing Techniques as a percentage of every work published that year, 0.10% in 2024.

still counting0.050%0.10%0.15%Natural Language Processing Techniques0.095% in 202519801990200020102020

Fractional works of Natural Language Processing Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Natural Language Processing Techniques9.4% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

Questions people ask

Where is natural language processing research done?

Natural Language Processing Techniques is a research topic within Artificial Intelligence. Science Explorer counts 89k research works in it since 1950. 9.7% of them reached the world's top 10% most cited for their field and year.

All 1,273 questions in the Question Catalogue

Discussion

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