The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Cross-lingual Name Tagging and Linking for 282 Languages
Open accessAbstract
The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia.Given a document in any of these languages, our framework is able to identify name mentions, assign a coarse-grained or fine-grained type to each mention, and link it to an English Knowledge Base (KB) if it is linkable.We achieve this goal by performing a series of new KB mining methods: generating "silver-standard" annotations by transferring annotations from English to other languages through crosslingual links and KB properties, refining annotations through self-training and topic selection, deriving language-specific morphology features from anchor links, and mining word translation pairs from crosslingual links.Both name tagging and linking results for 282 languages are promising on Wikipedia data and on-Wikipedia data.All the data sets, resources and systems for 282 languages are made publicly available as a new benchmark 1 .
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 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
Freebase: a collaboratively created graph database for structuring human knowledge
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., & Taylor, J. (2008) · Proceedings of the 2008 ACM SIGMOD international conference on Management of data · doi:10.1145/1376616.1376746 · Cited by 4,852
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
A Systematic Comparison of Various Statistical Alignment Models
Och, F. J., & Ney, H. (2003) · Computational Linguistics · doi:10.1162/089120103321337421 · Cited by 3,854
Word association norms, mutual information, and lexicography
Church, K., & Hanks, P. (1990) · doi:10.5555/89086.89095 · Cited by 3,590
Distant supervision for relation extraction without labeled data
Mintz, M., Bills, S., Snow, R., & Jurafsky, D. (2009) · Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2 - ACL-IJCNLP '09 · doi:10.3115/1690219.1690287 · Cited by 2,752
(2013) · Cited by 1,117
Normalized (pointwise) mutual information in collocation extraction
Bouma, G. (2009) · Cited by 791
Fine-Grained Entity Recognition
Ling, X., & Weld, D. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8122 · Cited by 476
Learning multilingual named entity recognition from Wikipedia
Nothman, J., Ringland, N., Radford, W., Murphy, T., & Curran, J. R. (2013) · Artificial Intelligence · doi:10.1016/j.artint.2012.03.006 · Cited by 360
Self-citation
Cited by
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer
Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., & Raffel, C. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2021.naacl-main.41 · Cited by 1,629
Power Hungry Processing: Watts Driving the Cost of AI Deployment?
Luccioni, S., Jernite, Y., & Strubell, E. (2024) · The 2024 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3630106.3658542 · Cited by 392
“…SD Prompts [46] IMDB [32] Rotten Tomatoes [39] SST 2 [48] maskedlanguagemodeling BookCorpus [59] C4 [42] OSCAR [37] WikiText [33] BookCorpus [59] OSCAR [37] Visual Genome [24] CPPE-5 [9] COCO [29] ReCoRD [53] WikiANN [38] CoNLL 2003 [50] Table 1: A list of the tasks and datasets used in our study.”
ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models
Xue, L., Barua, A., Constant, N., Al-Rfou, R., Narang, S., Kale, M., Roberts, A., & Raffel, C. (2022) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00461 · Cited by 330
How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models
Rust, P., Pfeiffer, J., Vulić, I., Ruder, S., & Gurevych, I. (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.243 · Cited by 202
X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
Jiang, Z., Anastasopoulos, A., Araki, J., Ding, H., & Neubig, G. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.479 · Cited by 88
Metadata
- Authors
- Pan, X., Zhang, B., May, J., Nothman, J., Knight, K., & Ji, H.
- Venue
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2017
- Pages
- 1946-1958
- OpenAlex
- W2742113707
- Cited by
- 312
- References
- 34
- FWCI
- 7.11
- 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: Cross-lingual Name Tagging and Linking for 282 Languages. https://app.cochat.ai/papers/10.18653/v1/p17-1178
- The paper
- Pan, X., Zhang, B., May, J., Nothman, J., Knight, K., & Ji, H. (2017). Cross-lingual Name Tagging and Linking for 282 Languages. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/p17-1178
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.
