The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons
Open accessAbstract
Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998).
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
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Self-citationA Maximum Entropy Model for Part-Of-Speech Tagging
Ratnaparkhi, A. (1996) · Cited by 1,317
Shallow parsing with conditional random fields
Sha, F., & Pereira, F. (2003) · Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL '03 · doi:10.3115/1073445.1073473 · Cited by 1,246
Inducing features of random fields
Della Pietra, S., Della Pietra, V., & Lafferty, J. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.588021 · Cited by 1,036
Unsupervised Models for Named Entity Classification
Collins, M., & Singer, Y. (1999) · Cited by 819
A comparison of algorithms for maximum entropy parameter estimation
Malouf, R. (2002) · proceeding of the 6th conference on Natural language learning - COLING-02 · doi:10.3115/1118853.1118871 · Cited by 655
Efficiently Inducing Features of Conditional Random Fields
McCallum, A. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1212.2504 · Cited by 363
Self-citationExploiting diverse knowledge sources via maximum entropy in named entity recognition
Borthwick, A., Sterling, J., Agichtein, E., & Grishman, R. (1998) · Cited by 207
Bootstrapping for text learning tasks
Riloff, E. (1999) · Cited by 90
Cited by
Natural Language Processing (almost) from Scratch
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., & Kuksa, P. P. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1103.0398 · Cited by 4,962
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 survey of named entity recognition and classification
Nadeau, D., & Sekine, S. (2007) · Lingvisticae Investigationes · doi:10.1075/li.30.1.03nad · Cited by 2,507
Savova, G. K., Masanz, J. J., Ogren, P. V., Zheng, J., Sohn, S., Kipper-Schuler, K. C., & Chute, C. G. (2010) · Journal of the American Medical Informatics Association · doi:10.1136/jamia.2009.001560 · Cited by 1,967
(2004) · Cited by 610
Metadata
- Authors
- McCallum, A., & Li, W.
- Venue
- Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -
- Published
- 2003
- Volume
- 4
- Pages
- 188-191
- OpenAlex
- W2141099517
- Cited by
- 1,160
- References
- 9
- FWCI
- 17.43
- 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: Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. https://app.cochat.ai/papers/10.3115/1119176.1119206
- The paper
- McCallum, A., & Li, W. (2003). Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons. Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 -. https://doi.org/10.3115/1119176.1119206
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.
