NLTK: The Natural Language Toolkit
Open accessAbstract
The Natural Language Toolkit is a suite of program modules, data sets and tutorials supporting research and teaching in computational linguistics and natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past year the toolkit has been rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python language. This paper reports on the simplified toolkit and explains how it is used in teaching NLP.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 5
- Marti Hearst. 2005. Teaching applied natural language processing: Triumphs and tribulations. In Proc 2nd ACL Workshop on Effective Tools and Methodolo- gies for Teaching NLP and CL, pages 1-8, ACL
- Elizabeth Liddy and Nancy McCracken. 2005. Hands- on NLP for an interdisciplinary audience. In Proc 2nd ACL Workshop on Effective Tools and Method- ologies for Teaching NLP and CL, pages 62-68, ACL
- Edward Loper and Steven Bird. 2002. NLTK: The Natural Language Toolkit. In Proc ACL Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, pages 62-69. ACL.
- David Beasley. 2006. Python Essential Reference, 3rd Edition. Sams.
- Rune Sætre, Amund Tveit, Tonje S. Steigedal, and Astrid Lægreid. 2005. Semantic annotation of biomedical literature using Google. In Data Min- ing and Bioinformatics Workshop, volume 3482 of Lecture Notes in Computer Science. Springer.
Cited by
Sorted by citations
Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D. A., et al. (2017) · International Journal of Computer Vision · doi:10.1007/s11263-016-0981-7 · Cited by 5,107 · see citing works
“We lowercase, lemmatize (Bird 2006), and strip excess whitespace from all relationships.”
BackgroundInfluentialPaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590 · see citing works
“An off-the-shelf POS tagger (Bird & Loper, 2004) is used to remove stop words and select only descriptive words (i.e. adjectives and adverbs).”
MethodInfluentialDatasets: A Community Library for Natural Language Processing
Lhoest, Q., Villanova del Moral, A., Jernite, Y., Thakur, A., von Platen, P., Patil, S., Chaumond, J., Drame, M., Plu, J., Tunstall, L., et al. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2021.emnlp-demo.21 · Cited by 343 · see citing works
“The influential NLTK project (Bird, 2006) provided a data library that makes it easy to download and access core datasets.”
BackgroundGender and Cross-Cultural Differences in Social Media Disclosures of Mental Illness
De Choudhury, M., Sharma, S. S., Logar, T., Eekhout, W., & Nielsen, R. C. (2017) · Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing · doi:10.1145/2998181.2998220 · Cited by 178 · see citing works
Wang, X., & Qiu, X. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103777 · Cited by 118 · see citing works
A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation
Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., & Dolan, B. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.464 · Cited by 63 · see citing works
Using Learning Analytics for Preserving Academic Integrity
Amigud, A., Arnedo-Moreno, J., Daradoumis, T., & Guerrero-Roldan, A.-E. (2017) · The International Review of Research in Open and Distributed Learning · doi:10.19173/irrodl.v18i5.3103 · Cited by 61 · see citing works
Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks
Cotroneo, D., Improta, C., Liguori, P., & Natella, R. (2024) · Proceedings of the 32nd IEEE/ACM International Conference on Program Comprehension · doi:10.1145/3643916.3644416 · Cited by 45 · see citing works
Kwon, S., & Park, A. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2021.107087 · Cited by 33 · see citing works
Studying the difference between natural and programming language corpora
Casalnuovo, C., Sagae, K., & Devanbu, P. (2019) · Empirical Software Engineering · doi:10.1007/s10664-018-9669-7 · Cited by 33 · see citing works
Fukuoka, Y., Lindgren, T. G., Mintz, Y. D., Hooper, J., & Aswani, A. (2018) · JMIR mHealth and uHealth · doi:10.2196/10042 · Cited by 29 · see citing works
Lin, T.-L., Chen, W.-C., Hsiao, T.-F., Liu, H.-I., Yeh, Y.-H., Chan, Y.-K., Lien, W.-S., Kuo, P.-Y., Yu, P. S., & Shuai, H.-H. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025 · doi:10.18653/v1/2025.findings-emnlp.259 · Cited by 6
GameTileNet: A Semantic Dataset for Low-Resolution Game Art in Procedural Content Generation
Chen, Y.-C., & Jhala, A. (2025) · arXiv · doi:10.48550/arxiv.2507.02941
“In the preprocessing steps, we utilize the Natural Language Toolkit (NLTK) for initial cleaning [3, 17].”
- Authors
- Bird, S.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- Bird, Steven
- ORCID
- Bird, Steven
- Venue
- Proceedings of the COLING/ACL on Interactive presentation sessions -
- Published
- 2006
- Pages
- 69-72
- OpenAlex
- W4251372957
- Cited by
- 841
- References
- 0
- Citations by year
- 2026: 48 · 2025: 85 · 2024: 98 · 2023: 88 · 2022: 71 · 2021: 83 · 2020: 33 · 2019: 32 · 2018: 76 · 2017: 85 · 2016: 48 · 2015: 48 · 2014: 20 · 2013: 16 · 2012: 4
- FWCI
- 0.47
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Conference on Computational Linguistics and Annual Meeting of the Association for Computational Linguistics (COLING/ACL) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Computational Physics and Python Applications
- Keywords
- NLTK, natural language processing, computational linguistics, Python, open source software
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
