The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
A practical part-of-speech tagger
Open accessAbstract
We present an implementation of a part-of-speech tagger based on a hidden Markov model. The methodology enables robust and accurate tagging with few resource requirements. Only a lexicon and some unlabeled training text are required. Accuracy exceeds 96%. We describe implementation strategies and optimizations which result in high-speed operation. Three applications for tagging are described: phrase recognition; word sense disambiguation; and grammatical function assignment.
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
An introduction to hidden Markov models
Rabiner, L., & Juang, B. (1986) · IEEE ASSP Magazine · doi:10.1109/massp.1986.1165342 · Cited by 4,775
Cited by
TnT: a statistical part-of-speech tagger
Brants, T. (2000) · Proceedings of the sixth conference on Applied natural language processing - · doi:10.3115/974147.974178 · Cited by 1,329
Improvements in Part-of-Speech Tagging with an Application to German
Schmid, H. (1999) · Text, Speech and Language Technology · doi:10.1007/978-94-017-2390-9_2 · Cited by 819
Using Language Models for Information Retrieval
Hiemstra, D. (2001) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.570441 · Cited by 448
Metadata
- Authors
- Cutting, D., Kupiec, J., Pedersen, J., & Sibun, P.
- Venue
- Proceedings of the third conference on Applied natural language processing -
- Published
- 1992
- Pages
- 133
- OpenAlex
- W2046224275
- Cited by
- 618
- References
- 1
- FWCI
- 28.50
- Type
- conference paper
- Language
- en
- Open access
- bronze
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: A practical part-of-speech tagger. https://app.cochat.ai/papers/10.3115/974499.974523
- The paper
- Cutting, D., Kupiec, J., Pedersen, J., & Sibun, P. (1992). A practical part-of-speech tagger. Proceedings of the third conference on Applied natural language processing -. https://doi.org/10.3115/974499.974523
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.
