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. 21.2% 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%
- 19k per year above
- Top-10% rate
- 21.2% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +36% the tick is no change
Which countries lead Natural Language Processing Techniques research?
By volume, China and the United States publish the most (5k and 2.9k works in 2022–2025).
By volume, 2022–2025
- 1 China 5k works
- 2 United States 2.9k works
- 3 India 1.6k works
- 4 Germany 843 works
- 5 France 712 works
- 6 United Kingdom 705 works
- 7 Japan 698 works
- 8 Canada 414 works
- 9 South Korea 393 works
- 10 Italy 385 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.
Shares of the rows listed above, not of the whole node.
Which institutions lead Natural Language Processing Techniques research?
By volume in 2022–2025, Peking University publishes the most Natural Language Processing Techniques research, followed by Tsinghua University and Shanghai Jiao Tong University.
By volume, 2022–2025
- 1 Peking UniversityChina 116 works
- 2 Tsinghua UniversityChina 110 works
- 3 Shanghai Jiao Tong UniversityChina 97 works
- 4 Carnegie Mellon UniversityUnited States 92 works
- 5 Beijing University of Posts and TelecommunicationsChina 90 works
- 6 Harbin Institute of TechnologyChina 85 works
- 7 Zhejiang UniversityChina 82 works
- 8 Google (United States)United States 81 works
- 9 University of Science and Technology of ChinaChina 75 works
- 10 Fudan UniversityChina 72 works
Who are the leading researchers in Natural Language Processing Techniques?
The most-cited researchers publishing on Natural Language Processing Techniques include Andrew Zisserman, Geoffrey E. Hinton and Ilya Sutskever.
- 1 Andrew Zisserman United Kingdom 25k citations
- 2 Geoffrey E. Hinton Canada 22k citations
- 3 Ilya Sutskever United States 21k citations
- 4 Yoshua Bengio Canada 17k citations
- 5 Serge Belongie United States 14k citations
- 6 Wei Liu China 9.7k citations
- 7 Wei Liu Australia 9.7k citations
- 8 Ion Stoica United States 9.6k citations
- 9 Zhiheng Huang Germany 9k citations
- 10 Quoc V. Le United States 8.6k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Natural Language Processing Techniques research done?
The largest centres of Natural Language Processing Techniques research in 2022–2025 are Beijing (China), Shanghai (China), Tokyo (Japan) and Guangzhou (China). 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, 2022–2025
Where it is the local speciality
- RedmondUS · 41.5 works22×
- San MateoUS · 22.0 works21×
- IkomaJP · 27.0 works17×
Location quotient: how much more of its research is in Natural Language Processing Techniques than the world average.
Where is the best place to study Natural Language Processing Techniques?
Among universities, judged by research, Singapore Management University, Carnegie Mellon University and Mohamed bin Zayed University of Artificial Intelligence score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Singapore Management UniversitySingapore | 69.0 | 35.3% | 10.0× | 21 | +125.9% |
| 2 | Carnegie Mellon UniversityUnited States | 66.9 | 26.8% | 11.5× | 92 | +26.2% |
| 3 | Mohamed bin Zayed University of Artificial IntelligenceUnited Arab Emirates | 65.7 | 31.2% | 28.7× | 23 | — |
| 4 | Nanyang Technological UniversitySingapore | 59.6 | 38.2% | 3.4× | 54 | +80.9% |
| 5 | Singapore University of Technology and DesignSingapore | 58.9 | 24.1% | 8.0× | 15 | +330.3% |
| 6 | Beijing University of Posts and TelecommunicationsChina | 56.7 | 13.6% | 7.4× | 90 | +140.6% |
| 7 | Guangdong University of Foreign StudiesChina | 54.6 | 11.9% | 16.0× | 33 | +141.0% |
| 8 | University of Hong KongHong Kong | 52.4 | 33.0% | 2.2× | 37 | +111.5% |
| 9 | Hong Kong Polytechnic UniversityHong Kong | 52.3 | 32.4% | 3.2× | 54 | +15.6% |
| 10 | Amrita Vishwa VidyapeethamIndia | 51.9 | 15.4% | 5.7× | 60 | +171.5% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Natural Language Processing Techniques research growing?
Output in 2018–2022 was 36% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Natural Language Processing Techniques.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
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.