The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A Neural Layered Model for Nested Named Entity Recognition
Open accessAbstract
Meizhi Ju, Makoto Miwa, Sophia Ananiadou. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.
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
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
The Stanford CoreNLP Natural Language Processing Toolkit
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014) · Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.3115/v1/p14-5010 · Cited by 7,037
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Backpropagation through time: what it does and how to do it
Werbos, P. J. (1990) · Proceedings of the IEEE · doi:10.1109/5.58337 · Cited by 4,879
Neural Architectures for Named Entity Recognition
Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K., & Dyer, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1030 · Cited by 4,401
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Ma, X., & Hovy, E. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1101 · Cited by 2,567
End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures
Miwa, M., & Bansal, M. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1105 · Cited by 1,322
Self-citationGENIA corpus—a semantically annotated corpus for bio-textmining
Kim, J.-D., Ohta, T., Tateisi, Y., & Tsujii, J. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg1023 · Cited by 1,232
Cited by
A Unified MRC Framework for Named Entity Recognition
Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.519 · Cited by 697
Named Entity Recognition as Dependency Parsing
Yu, J., Bohnet, B., & Poesio, M. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.577 · Cited by 458
Unified Named Entity Recognition as Word-Word Relation Classification
Li, J., Fei, H., Liu, J., Wu, S., Zhang, M., Teng, C., Ji, D., & Li, F. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21344 · Cited by 394
A Unified Generative Framework for Various NER Subtasks
Yan, H., Gui, T., Dai, J., Guo, Q., Zhang, Z., & Qiu, X. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.451 · Cited by 299
“, 2019; Metke-Jimenez and Karimi, 2016) or multi-level (Ju et al., 2018; Fisher and Vlachos, 2019; Shibuya and Hovy, 2020).”
MethodEvaluating the carbon footprint of NLP methods: a survey and analysis of existing tools
Bannour, N., Ghannay, S., Névéol, A., & Ligozat, A.-L. (2021) · Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing · doi:10.18653/v1/2021.sustainlp-1.2 · Cited by 98
Metadata
- Authors
- Ju, M., Miwa, M., & Ananiadou, S.
- Venue
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Published
- 2018
- Pages
- 1446-1459
- OpenAlex
- W2804221886
- Cited by
- 309
- References
- 33
- FWCI
- 14.04
- Type
- conference paper
- Language
- en
- Open access
- hybrid
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 Neural Layered Model for Nested Named Entity Recognition. https://app.cochat.ai/papers/10.18653/v1/n18-1131
- The paper
- Ju, M., Miwa, M., & Ananiadou, S. (2018). A Neural Layered Model for Nested Named Entity Recognition. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). https://doi.org/10.18653/v1/n18-1131
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.
