The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
NLTK: the natural language toolkit
Open accessAbstract
The Natural Language Toolkit is a suite of program modules, data sets, tutorials and exercises, covering symbolic and statistical natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past three years, NLTK has become popular in teaching and research. We describe the toolkit and report on its current state of development.
Overview
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
Verification
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.
Citations
Cites
NLTK: The Natural Language Toolkit
Loper, E., & Bird, S. (2002) · arXiv (Cornell University) · doi:10.48550/arxiv.cs/0205028 · Cited by 1,844
Self-citation(2009) · Chapman & Hall/CRC Mathematical & Computational Biology · doi:10.1201/9781584889304.axd · Cited by 410
Cited by
PaLM: 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
Neural Network Acceptability Judgments
Warstadt, A., Singh, A., & Bowman, S. R. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00290 · Cited by 1,215
A high-performance neuroprosthesis for speech decoding and avatar control
Metzger, S. L., Littlejohn, K. T., Silva, A. B., Moses, D. A., Seaton, M. P., Wang, R., Dougherty, M. E., Liu, J. R., Wu, P., Berger, M. A., et al. (2023) · Nature · doi:10.1038/s41586-023-06443-4 · Cited by 522
Health system-scale language models are all-purpose prediction engines
Jiang, L. Y., Liu, X. C., Nejatian, N. P., Nasir-Moin, M., Wang, D., Abidin, A., Eaton, K., Riina, H. A., Laufer, I., Punjabi, P., et al. (2023) · Nature · doi:10.1038/s41586-023-06160-y · Cited by 460
Multimodal Speech Emotion Recognition Using Audio and Text
Yoon, S., Byun, S., & Jung, K. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639583 · Cited by 351
Metadata
- Authors
- Bird, S., & Loper, E.
- Venue
- Proceedings of the ACL 2004 on Interactive poster and demonstration sessions -
- Published
- 2004
- Pages
- 31-es
- OpenAlex
- W4245267204
- Cited by
- 403
- References
- 2
- FWCI
- 0.43
- Type
- conference paper
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: NLTK: the natural language toolkit. https://app.cochat.ai/papers/10.3115/1219044.1219075
- The paper
- Bird, S., & Loper, E. (2004). NLTK: the natural language toolkit. Proceedings of the ACL 2004 on Interactive poster and demonstration sessions -. https://doi.org/10.3115/1219044.1219075
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
