Skip to main content
SpringerLink
Log in

NEREL: a Russian information extraction dataset with rich annotation for nested entities, relations, and wikidata entity links

  • Original Paper
  • Published:
Language Resources and Evaluation Aims and scope Submit manuscript

Abstract

This paper describes NEREL—a Russian news dataset suited for three tasks: nested named entity recognition, relation extraction, and entity linking. Compared to flat entities, nested named entities provide a richer and more complete annotation while also increasing the coverage of relations annotation and entity linking. Relations between nested named entities may cross entity boundaries to connect to shorter entities nested within longer ones, which makes it harder to detect such relations. NEREL is currently the largest Russian dataset annotated with entities and relations: it comprises 29 named entity types and 49 relation types. At the time of writing, the dataset contains 56 K named entities and 39 K relations annotated in 933 person-oriented news articles. NEREL is annotated with relations at three levels: (1) within nested named entities, (2) within sentences, and (3) with relations crossing sentence boundaries. We provide benchmark evaluation of current state-of-the-art methods in all three tasks. The dataset is freely available at https://github.com/nerel-ds/NEREL.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. https://www.ethnologue.com/.

  2. https://ru.wikinews.org/.

  3. https://multiconer.github.io.

  4. AIDA: https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/research/ambiverse-nlu/aida.

  5. Some relations do not have counterparts in Wikidata properties. For example, age and age_died_at occur in texts, while Wikidata has only date of birth (P569) and date of death (P570) that allow calculate the above mentioned age values.

  6. For example, the phrase in bold in the sentence Natalia accepted Pushkin’s proposal, and in April 1830, she became the wife of the famous Russian poet Alexander Pushkin should be linked to the Wikidata’s marriage (Q8445).

  7. https://github.com/vladislavneon/kbqa-tools/.

  8. Among them, there are many linkages to year or month entities, e.g. October 2009 \(\rightarrow\) Q243251, that are not very helpful for our task.

  9. https://github.com/natasha/natasha.

  10. https://spacy.io.

  11. https://stanfordnlp.github.io/stanza/.

  12. https://huggingface.co/DeepPavlov/rubert-base-cased.

  13. The list of ‘wikititles+muse’ pairs can be found here: https://github.com/cambridgeltl/sapbert/tree/main/training_data.

References

  • Alt, C., Hübner, M., & Hennig, L. (2019). Improving relation extraction by pre-trained language representations. Preprint at http://arxiv.org/abs/1906.03088

  • Benikova, D., Biemann, C., & Reznicek, M. (2014). Nosta-d named entity annotation for German: Guidelines and dataset. In LREC (pp. 2524–2531).

  • Bentivogli, L., Forner, P., Giuliano, C., Marchetti, A., Pianta, E., & Tymoshenko, K. (2010). Extending English ace 2005 corpus annotation with ground-truth links to Wikipedia. In Proceedings of the 2nd workshop on the people’s web meets NLP: Collaboratively constructed semantic resources (pp. 19–27).

  • Bies, A., Song, Z., Getman, J., Ellis, J., Mott, J., Strassel, S., Palmer, M., Mitamura, T., Freedman, M., Ji, H., & O’gorman, T. (2016). A comparison of event representations in deft. In Proceedings of the fourth workshop on events (pp. 27–36).

  • Bollacker, K., Cook, R., & Tufts, P. (2007). Freebase: A shared database of structured general human knowledge. In AAAI (vol. 7, pp. 1962–1963).

  • Botha, J. A., Shan, Z., & Gillick, D. (2020). Entity linking in 100 languages. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) (pp. 7833–7845).

  • Brandsen, A., Verberne, S., Lambers, K., Wansleeben, M., Calzolari, N., Béchet, F., Blache, P., Choukri, K., Cieri, C., Declerck, T., & Goggi, G. (2020). Creating a dataset for named entity recognition in the archaeology domain. In Conference proceedings LREC 2020 (pp. 4573–4577). The European Language Resources Association.

  • Bruches, E., Mezentseva, A., & Batura, T. (2021). A system for information extraction from scientific texts in Russian. Preprint at http://arxiv.org/abs/2109.06703

  • Campillos-Llanos, L., Valverde-Mateos, A., Capllonch-Carrión, A., & Moreno-Sandoval, A. (2021). A clinical trials corpus annotated with umls entities to enhance the access to evidence-based medicine. BMC Medical Informatics and Decision Making, 21(1), 1–19.

    Google Scholar 

  • Cao, N. D., Wu, L., Popat, K., Artetxe, M., Goyal, N., Plekhanov, M., Zettlemoyer, L., Cancedda, N., Riedel, S., & Petroni, F. (2021). Multilingual autoregressive entity linking. Preprint at https://arxiv.org/abs/2103.12528

  • Checco, A., Roitero, K., Maddalena, E., Mizzaro, S., & Demartini, G. (2017). Let’s agree to disagree: Fixing agreement measures for crowdsourcing. In Fifth AAAI conference on human computation and crowdsourcing (pp. 11–20).

  • Eberts, M., & Ulges, A. (2020). Span-based joint entity and relation extraction with transformer pre-training. In ECAI.

  • Ellis, J. (2012). TAC KBP entity selection. KBP 2012 guidelines. Version 1.1. Linguistic Data Consortium (2012). Retrieved from https://tac.nist.gov/2012/KBP/task_guidelines/TAC_KBP_Entity_Selection_V1.1.pdf

  • Ellis, J., Getman, J., Fore, D., Kuster, N., Song, Z., Bies, A., & Strassel, S. M. (2015). Overview of linguistic resources for the tac kbp 2015 evaluations: Methodologies and results. In TAC. Retrieved from https://tac.nist.gov/publications/2015/additional.papers/TAC2015.KBP_resources_overview.proceedings.pdf

  • Ellis, J., Getman, J., & Strassel, S. M. (2014). Overview of linguistic resources for the tac kbp 2014 evaluations: Planning, execution, and results. In Proceedings of TAC KBP 2014 Workshop, National Institute of Standards and Technology (pp. 17–18). Retrieved from https://www.ldc.upenn.edu/sites/www.ldc.upenn.edu/files/tackbp-2014-overview.pdf

  • Finkel, J. R., Grenager, T., & Manning, C. D. (2005). Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43rd annual meeting of the Association for Computational Linguistics (ACL’05) (pp. 363–370).

  • Gabrilovich, E., Ringgaard, M., & Subramanya, A. (2013). FACC1: Freebase annotation of ClueWeb corpora, version 1 (release date 2013-06-26, format version 1, correction level 0). Retrieved from http://lemurproject.org/clueweb09/

  • Gareev, R., Tkachenko, M., Solovyev, V., Simanovsky, A., & Ivanov, V. (2013). Introducing baselines for Russian named entity recognition. In International conference on intelligent text processing and computational linguistics (pp. 329–342).

  • Getman, J., Ellis, J., Song, Z., Tracey, J., & Strassel, S. M. (2017). Overview of linguistic resources for the tac kbp 2017 evaluations: Methodologies and results. In TAC.

  • Gordeev, D., Davletov, A., Rey, A., Akzhigitova, G., & Geymbukh, G. (2020). Relation extraction dataset for the Russian language. In Computational linguistics and intellectual technologies: Proceedings of the international conference “dialog” [Komp’iuternaia Lingvistika i Intellektual’nye Tehnologii: Trudy Mezhdunarodnoj Konferentsii “Dialog”].

  • Guo, Z., & Barbosa, D. (2018). Robust named entity disambiguation with random walks. Semantic Web, 9(4), 459–479.

    Article  Google Scholar 

  • Hamdi, A., Linhares Pontes, E., Boros, E., Nguyen, T. T. H., Hackl, G., Moreno, J. G., & Doucet, A. (2021). A multilingual dataset for named entity recognition, entity linking and stance detection in historical newspapers. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (pp. 2328–2334).

  • Han, X., Gao, T., Yao, Y., Ye, D., Liu, Z., & Sun, M. (2019). Opennre: An open and extensible toolkit for neural relation extraction. EMNLP-IJCNLP, 2019, 169.

    Google Scholar 

  • Hendrickx, I., Kim, S. N., Kozareva, Z., Nakov, P., Ó Séaghdha, D., Padó, S., Pennacchiotti, M., Romano, L., & Szpakowicz, S. (2010). SemEval-2010 task 8: Multi-way classification of semantic relations between pairs of nominals. In Proceedings of the 5th international workshop on semantic evaluation (pp. 33–38). Association for Computational Linguistics. Retrieved from https://www.aclweb.org/anthology/S10-1006

  • Hoffart, J., Suchanek, F. M., Berberich, K., & Weikum, G. (2013). Yago2: A spatially and temporally enhanced knowledge base from Wikipedia. Artificial Intelligence, 194, 28–61. https://doi.org/10.1016/j.artint.2012.06.001

    Article  Google Scholar 

  • Hoffart, J., Yosef, M. A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., & Weikum, G. (2011). Robust disambiguation of named entities in text. In Proceedings of the conference on empirical methods in natural language processing, EMNLP ’11 (pp. 782–792). Association for Computational Linguistics. Retrieved from http://dl.acm.org/citation.cfm?id=2145432.2145521

  • Hovy, E., Marcus, M., Palmer, M., Ramshaw, L., & Weischedel, R. (2006). Ontonotes: The 90% solution. In Proceedings of the human language technology conference of the NAACL, companion volume: Short papers, NAACL-Short ’06 (pp. 57–60). Association for Computational Linguistics.

  • Ivanin, V., Artemova, E., Batura, T., Ivanov, V., Sarkisyan, V., Tutubalina, E., & Smurov, I. (2020). Rurebus-2020 shared task: Russian relation extraction for business. In Computational linguistics and intellectual technologies: Proceedings of the international conference “dialog” [Komp’iuternaia Lingvistika i Intellektual’nye Tehnologii: Trudy Mezhdunarodnoj Konferentsii “Dialog”].

  • Ji, H., Nothman, J., Hachey, B., & Florian, R. (2015). Overview of tac-kbp2015 tri-lingual entity discovery and linking. In TAC.

  • Jiang, H., Hua, Y., Beeferman, D., & Roy, D. (2022). Annotating the tweebank corpus on named entity recognition and building nlp models for social media analysis. In Conference proceedings LREC 2022 (pp. 7199–7208).

  • Joshi, M., Chen, D., Liu, Y., Weld, D. S., Zettlemoyer, L., & Levy, O. (2020). Spanbert: Improving pre-training by representing and predicting spans. Transactions of the Association for Computational Linguistics, 8, 64–77.

    Article  Google Scholar 

  • Jue, W., Shou, L., Chen, K., & Chen, G. (2020). Pyramid: A layered model for nested named entity recognition. In Proceedings of the 58th annual meeting of the Association for Computational Linguistics (pp. 5918–5928).

  • Korablinov, V., & Braslavski, P. (2020). RuBQ: A Russian dataset for question answering over Wikidata. In ISWC (pp. 97–110).

  • Krippendorff, K. (2004). Reliability in content analysis: Some common misconceptions and recommendations. Human Communication Research, 30(3), 411–433.

    Google Scholar 

  • Kuratov, Y., & Arkhipov, M. (2019). Adaptation of deep bidirectional multilingual transformers for Russian language. Preprint at http://arxiv.org/abs/1905.07213

  • Kuznetsov, A., Braslavski, P., & Ivanov, V. (2016). Family matters: Company relations extraction from wikipedia. In International conference on knowledge engineering and the semantic web (pp. 81–92). Springer.

  • Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., & Li, J. (2020). A unified MRC framework for named entity recognition. In Proceedings of the 58th annual meeting of the Association for Computational Linguistics (pp. 5849–5859).

  • Ling, X., Singh, S., & Weld, D. S. (2015). Design challenges for entity linking. Transactions of the Association for Computational Linguistics, 3, 315–328.

    Article  Google Scholar 

  • Liu, F., Vulić, I., Korhonen, A., & Collier, N. (2021). Learning domain-specialised representations for cross-lingual biomedical entity linking. In Proceedings of ACL-IJCNLP 2021 (pp. 565–574).

  • Loukachevitch, N., Artemova, E., Batura, T., Braslavski, P., Denisov, I., Ivanov, V., Manandhar, S., Pugachev, A., & Tutubalina, E. (2021). Nerel: A Russian dataset with nested named entities, relations and events. In Proceedings of the international conference on recent advances in natural language processing (RANLP 2021) (pp. 876–885).

  • Luan, Y., He, L., Ostendorf, M., & Hajishirzi, H. (2018). Multi-task identification of entities, relations, and coreference for scientific knowledge graph construction. In Proceedings of the 2018 conference on empirical methods in natural language processing (pp. 3219–3232).

  • Malmasi, S., Fang, A., Fetahu, B., Kar, S., & Rokhlenko, O. (2022). MultiCoNER: A large-scale multilingual dataset for complex named entity recognition. In Proceedings of the 29th international conference on computational linguistics (pp. 3798–3809).

  • Mendes, P. N., Jakob, M., García-Silva, A., & Bizer, C. (2011). Dbpedia spotlight: Shedding light on the web of documents. In Proceedings of the 7th international conference on semantic systems (pp. 1–8).

  • Mikolov, T., Grave, É., Bojanowski, P., Puhrsch, C., & Joulin, A. (2018). Advances in pre-training distributed word representations. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).

  • Milne, D., & Witten, I. H. (2008). Learning to link with Wikipedia. In Proceedings of the 17th ACM conference on information and knowledge management, CIKM ’08 (pp. 509–518). ACM. https://doi.org/10.1145/1458082.1458150

  • Mitamura, T., Liu, Z., & Hovy, E. H. (2015a). Overview of tac kbp 2015 event nugget track. In TAC.

  • Mitamura, T., Yamakawa, Y., Holm, S., Song, Z., Bies, A., Kulick, S., & Strassel, S. (2015b). Event nugget annotation: Processes and issues. In Proceedings of the the 3rd workshop on EVENTS: Definition, detection, coreference, and representation (pp. 66–76).

  • Moro, A., & Navigli, R. (2015). Semeval-2015 task 13: Multilingual all-words sense disambiguation and entity linking. In Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015) (pp. 288–297).

  • Mozharova, V., & Loukachevitch, N. (2016). Two-stage approach in Russian named entity recognition. InInternational FRUCT Conference on Intelligence, Social Media and Web (ISMW FRUCT) (pp. 1–6).

  • Nasar, Z., Jaffry, S. W., & Malik, M. K. (2021). Named entity recognition and relation extraction: State-of-the-art. ACM Computing Surveys, 54(1), 1–39.

    Article  Google Scholar 

  • Piskorski, J., Babych, B., Kancheva, Z., Kanishcheva, O., Lebedeva, M., Marcinczuk, M., Nakov, P., Osenova, P., Pivovarova, L., Pollak, S., Radev, I., Robnik-Sikonja, M., Starko, V., Steinberger, J., & Yangarber, R. (2021). Slav-ner: The 3rd cross-lingual challenge on recognition, normalization, classification, and linking of named entities across slavic languages. In Proceedings of the 8th workshop on Balto-Slavic natural language processing (pp. 122–133).

  • Piskorski, J., Laskova, L., Marcińczuk, M., Pivovarova, L., Přibáň, P., Steinberger, J., & Yangarber, R. (2019). The second cross-lingual challenge on recognition, normalization, classification, and linking of named entities across slavic languages. In Proceedings of the 7th workshop on Balto-Slavic natural language processing (pp. 63–74).

  • Plank, B., Jensen, K. N., & van der Goot, R. (2020). Dan+: Danish nested named entities and lexical normalization. In Proceedings of the 28th international conference on computational linguistics (pp. 6649–6662).

  • Ratinov, L., Roth, D., Downey, D., & Anderson, M. (2011). Local and global algorithms for disambiguation to Wikipedia. In Proceedings of the 49th annual meeting of the Association for Computational Linguistics: Human language technologies—Volume 1, HLT ’11 (pp. 1375–1384). Association for Computational Linguistics. Retrieved from http://dl.acm.org/citation.cfm?id=2002472.2002642

  • Ringland, N. (2015). Structured named entities. Doctor of philosophy Ph.D., School of Information Technologies. Retrieved from http://hdl.handle.net/2123/14558

  • Ringland, N., Dai, X., Hachey, B., Karimi, S., Paris, C., & Curran, J. R. (2019). Nne: A dataset for nested named entity recognition in english newswire. In Proceedings of the 57th annual meeting of the Association for Computational Linguistics (pp. 5176–5181).

  • Röder, M., Usbeck, R., & Ngomo, A. N. (2018). GERBIL—Benchmarking named entity recognition and linking consistently. Semantic Web, 9(5), 605–625. https://doi.org/10.3233/SW-170286

    Article  Google Scholar 

  • Rosales-Méndez, H., Hogan, A., & Poblete, B. (2018). Voxel: A benchmark dataset for multilingual entity linking. In International semantic web conference (pp. 170–186). Springer.

  • Ruokolainen, T., Kauppinen, P., Silfverberg, M., & Lindén, K. (2019). A finnish news corpus for named entity recognition. In Language resources and evaluation (pp. 1–26).

  • Sevgili, O., Shelmanov, A., Arkhipov, M., Panchenko, A., & Biemann, C. (2020). Neural entity linking: A survey of models based on deep learning. Preprint at http://arxiv.org/abs/2006.00575

  • Shabankhani, B., Charati, J. Y., Shabankhani, K., & Cherati, S. K. (2020). Survey of agreement between raters for nominal data using Krippendorff’s alpha. Archives of Pharmacy Practice, 10, 160–164.

    Google Scholar 

  • Shavrina, T., Fenogenova, A., Anton, E., Shevelev, D., Artemova, E., Malykh, V., Mikhailov, V., Tikhonova, M., Chertok, A., & Evlampiev, A. (2020). Russiansuperglue: A Russian language understanding evaluation benchmark. In Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP) (pp. 4717–4726).

  • Shibuya, T., & Hovy, E. (2020). Nested named entity recognition via second-best sequence learning and decoding. Transactions of the Association for Computational Linguistics, 8, 605–620.

    Article  Google Scholar 

  • Song, Z., Bies, A., Strassel, S., Riese, T., Mott, J., Ellis, J., Wright, J., Kulick, S., Ryant, N., & Ma, X. (2015). From light to rich ere: Annotation of entities, relations, and events. In Proceedings of the the 3rd workshop on EVENTS: Definition, detection, coreference, and representation (pp. 89–98).

  • Starostin, A., Bocharov, V., Alexeeva, S., Bodrova, A., Chuchunkov, A., Dzhumaev, S., Efimenko, I., Granovsky, D., Khoroshevsky, V., Krylova, I., Nikolaeva, M., Smurov, I., & Toldova, S. (2016). Factrueval 2016: Evaluation of named entity recognition and fact extraction systems for Russian. In Computational linguistics and intellectual technologies: Proceedings of the international conference “dialog” [Komp’iuternaia Lingvistika i Intellektual’nye Tehnologii: Trudy Mezhdunarodnoj Konferentsii “Dialog”] (pp. 702–720).

  • Stenetorp, P., Pyysalo, S., Topić, G., Ohta, T., Ananiadou, S., & Tsujii, J. (2012). Brat: a web-based tool for nlp-assisted text annotation. In Proceedings of the demonstrations at the 13th conference of the European chapter of the Association for Computational Linguistics (pp. 102–107).

  • Sysoev, A., & Nikishina, I. (2018). Smart context generation for disambiguation to Wikipedia. In Conference on artificial intelligence and natural language (pp. 11–22).

  • Thompson, P., Nawaz, R., McNaught, J., & Ananiadou, S. (2017). Enriching news events with meta-knowledge information. Language Resources and Evaluation, 51, 409–438.

    Article  Google Scholar 

  • Tjong Kim Sang, E.F., & De Meulder, F. (2003). Introduction to the conll-2003 shared task: Language-independent named entity recognition. In Proceedings of the seventh conference on natural language learning at HLT-NAACL 2003—Volume 4, CONLL ’03 (pp. 142–147). Association for Computational Linguistics.

  • Trofimov, I. (2014). Identification of personal names in news texts on collections persons-1000/1111-f (in Russian). In Proceedings of RCDL-2014 (pp. 217–221).

  • Vlasova, N., Lando, N., Suleymanova, E., & Trofimov, I. (2016). Situations-1000: A tagged corpus for event extraction from texts. In Computational linguistics and intellectual technologies: Proceedings of the international conference “dialog” [Komp’iuternaia Lingvistika i Intellektual’nye Tehnologii: Trudy Mezhdunarodnoj Konferentsii “Dialog”] (pp. 202–220).

  • Vlasova, N., Suleymanova, E., & Trofimov, I. (2014). Report on russian corpus for personal name retrieval. In Proceedings of TEL’2014 conference on computational and cognitive linguistics (pp. 36–40).

  • Vrandečić, D., & Krötzsch, M. (2014). Wikidata: A free collaborative knowledgebase. Communications of the ACM, 57(10), 78–85.

    Article  Google Scholar 

  • Walker, C., Strassel, S., Medero, J., & Maeda, K. (2006). ACE 2005 multilingual training corpus. LDC2006T06. Linguistic Data Consortium.

    Google Scholar 

  • Wang, H. C., Chen, C. C., & Li, T. W. (2022). Automatic content curation of news events. Multimedia Tools and Applications, 81(8), 10445–10467.

    Article  Google Scholar 

  • Weischedel, R., & Brunstein, A. (2005). Bbn pronoun coreference and entity type corpus. Philadelphia: Linguistic Data Consortium.

    Google Scholar 

  • Yao, Y., Ye, D., Li, P., Han, X., Lin, Y., Liu, Z., Liu, Z., Huang, L., Zhou, J., & Sun, M. (2019). Docred: A large-scale document-level relation extraction dataset. In Proceedings of the 57th annual meeting of the Association for Computational Linguistics (pp. 764–777).

  • Yu, J., Bohnet, B., & Poesio, M. (2020). Named entity recognition as dependency parsing. In Proceedings of the 58th annual meeting of the Association for Computational Linguistics (pp. 6470–6476).

  • Zaporojets, K., Deleu, J., Develder, C., & Demeester, T. (2021). Dwie: An entity-centric dataset for multi-task document-level information extraction. Information Processing & Management, 58(4), 102563.

    Article  Google Scholar 

  • Zhang, Y., Zhong, V., Chen, D., Angeli, G., & Manning, C. D. (2017). Position-aware attention and supervised data improve slot filling. In Proceedings of the 2017 conference on empirical methods in natural language processing (EMNLP 2017) (pp. 35–45).

  • Zhou, S., Rijhwani, S., & Neubig, G. (2019). Towards zero-resource cross-lingual entity linking. In Proceedings of the 2nd workshop on deep learning approaches for low-resource NLP (DeepLo 2019) (pp. 243–252). Association for Computational Linguistics. Retrieved from https://aclanthology.org/D19-6127

Download references

Acknowledgement

The work is supported by a grant for research centers in the field of artificial intelligence, provided by the Analytical Center for the Government of the Russian Federation in accordance with the subsidy agreement (agreement identifier 000000D730321P5Q0002) and the agreement with the Ivannikov Institute for System Programming of the Russian Academy of Sciences dated November 2, 2021 No. 70-2021-00142.

Author information

Authors and Affiliations

  1. Lomonosov Moscow State University, Moscow, Russia

    Natalia Loukachevitch, Igor Rozhkov, Artem Shelmanov & Alexey Yandutov

  2. ISP RAS Research Center for Trusted Artificial Intelligence, Moscow, Russia

    Natalia Loukachevitch

  3. HSE University, Moscow, Russia

    Ekaterina Artemova, Pavel Braslavski, Alexander Pugachev & Elena Tutubalina

  4. Novosibirsk State University, Novosibirsk, Russia

    Tatiana Batura

  5. A.P. Ershov Institute of Informatics Systems, Novosibirsk, Russia

    Tatiana Batura

  6. Ural Federal University, Yekaterinburg, Russia

    Pavel Braslavski

  7. Innopolis University, Innopolis, Russia

    Vladimir Ivanov

  8. Madan Bhandari University of Science and Technology, Chitlang, Nepal

    Suresh Manandhar

  9. AIRI, Moscow, Russia

    Artem Shelmanov & Elena Tutubalina

  10. Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE

    Artem Shelmanov

  11. Sber AI, Moscow, Russia

    Elena Tutubalina

Authors
  1. Natalia Loukachevitch
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ekaterina Artemova
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tatiana Batura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pavel Braslavski
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vladimir Ivanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Suresh Manandhar
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alexander Pugachev
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Igor Rozhkov
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Artem Shelmanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Elena Tutubalina
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Alexey Yandutov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Natalia Loukachevitch.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Loukachevitch, N., Artemova, E., Batura, T. et al. NEREL: a Russian information extraction dataset with rich annotation for nested entities, relations, and wikidata entity links. Lang Resources & Evaluation (2023). https://doi.org/10.1007/s10579-023-09674-z

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10579-023-09674-z

Keywords

Mathematics Subject Classification

Search

Navigation