The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Named entity recognition with character-level models
Open accessAbstract
We discuss two named-entity recognition models which use characters and character n-grams either exclusively or as an important part of their data representation. The first model is a character-level HMM with minimal context information, and the second model is a maximum-entropy conditional markov model with substantially richer context features. Our best model achieves an overall F1 of 86.07% on the English test data (92.31% on the development data). This number represents a 25% error reduction over the same model without word-internal (substring) features.
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
The hierarchical hidden Markov model: Analysis and applications
Fine, S., Singer, Y., & Tishby, N. (1998) · Machine Learning · doi:10.1023/a:1007469218079 · Cited by 906
Cited by
Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition
Tjong Kim Sang, E. F., & De Meulder, F. (2003) · Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - · doi:10.3115/1119176.1119195 · Cited by 2,788
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
Ando, R. K., & Zhang, T. (2005) · doi:10.5555/1046920.1194905 · Cited by 1,311
Aggarwal, C. C. (2015) · Data Mining · doi:10.1007/978-3-319-14142-8_13 · Cited by 606
Recent advances in biomedical literature mining
Zhao, S., Su, C., Lu, Z., & Wang, F. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa057 · Cited by 134
A Scoping Review of Adopted Information Extraction Methods for RCTs.
Aletaha, A., Nemati-Anaraki, L., Keshtkar, A., Sedghi, S., Keramatfar, A., & Korolyova, A. (2023) · Medical Journal of The Islamic Republic of Iran · doi:10.47176/mjiri.37.95 · Cited by 2
Metadata
- Authors
- Klein, D., Smarr, J., Nguyen, H., & Manning, C. D.
- Venue
- Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -
- Published
- 2003
- Volume
- 4
- Pages
- 180-183
- OpenAlex
- W2041614298
- Cited by
- 194
- References
- 1
- FWCI
- 8.59
- 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: Named entity recognition with character-level models. https://app.cochat.ai/papers/10.3115/1119176.1119204
- The paper
- Klein, D., Smarr, J., Nguyen, H., & Manning, C. D. (2003). Named entity recognition with character-level models. Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -. https://doi.org/10.3115/1119176.1119204
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.
