kgnlp/meld-open

MELD Open

MELD is a multilingual and multi-domain dataset for Named Entity Recognition (NER) constructed from 60 existing datasets. It includes gold-standard annotations across 60 languages and 14 domains. This dataset is a subset of 43 datasets for which licenses permit the redistribution of data in a new format. See the MELD GitHub repository for more details.

Note: This version of MELD Open retains the original labels from its source datasets. For normalized labels, use kgnlp/meld-open-normalized.

Attribution

Unless stated otherwise, each dataset in this repository has been processed and converted from its original source to the MELD format and is shared independently under its original license.

AgCNER

The AgCNER data was made available under the CC0 public domain dedication. The source dataset is available at https://springernature.figshare.com/collections/AgCNER_the_First_Large-Scale_Chinese_Named_Entity_Recognition_Dataset_for_Agricultural_Diseases_and_Pests/6807873.

References: Xiaochuang Yao, Xia Hao, Ruilin Liu, Lin Li, and Xuchao Guo. 2024. AgCNER, the first large-scale chinese named entity recognition dataset for agricultural diseases and pests. Scientific Data, 11(1):769.

AgriNER

The AgriNER data originally published by Sayan De, Debarshi Kumar Sanyal, and Imon Mukherjee is licensed under CC BY-SA 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/Tec4Tric/AgriNER.

References: Sayan De, Debarshi Kumar Sanyal, and Imon Mukherjee. 2023. AgriNER: An NER dataset of agricultural entities for the semantic web. In The semantic web: ESWC 2023 satellite events, pages 59–63, Cham. Springer Nature Switzerland.

AnatEM

The AgriNER data originally published by Sampo Pyysalo and Sophia Ananiadou is licensed under CC BY-SA 3.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://www.nactem.ac.uk/anatomytagger/#AnatEM.

References: Sampo Pyysalo and Sophia Ananiadou. 2013. Anatomical entity mention recognition at literature scale. Bioinformatics, 30(6):868–875.

BC2GM

The BC2GM data originally published by Larry L. Smith, Lorraine K. Tanabe, Rie Ando, Cheng-Ju Kuo, I-Fang Chung, Chun-Nan Hsu, Yu-Shi Lin, Roman Klinger, C. Friedrich, Kuzman Ganchev, Manabu Torii, Hongfang Liu, Barry Haddow, Craig A. Struble, Richard J. Povinelli, Andreas Vlachos, William A. Baumgartner, Lawrence E. Hunter, Bob Carpenter, and others is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/cambridgeltl/MTL-Bioinformatics-2016/tree/master/data/BC2GM-IOB.

References: Larry L. Smith, Lorraine K. Tanabe, Rie Ando, Cheng-Ju Kuo, I-Fang Chung, Chun-Nan Hsu, Yu-Shi Lin, Roman Klinger, C. Friedrich, Kuzman Ganchev, Manabu Torii, Hongfang Liu, Barry Haddow, Craig A. Struble, Richard J. Povinelli, Andreas Vlachos, William A. Baumgartner, Lawrence E. Hunter, Bob Carpenter, et al. 2008. Overview of BioCreative II gene mention recognition. Genome Biology, 9:S2–S2.

BC5CDR

The BC5CDR data has been dedicated to the public domain. The source dataset is available at https://www.ncbi.nlm.nih.gov/research/bionlp/Data.

References: Jiao Li, Yueping Sun, Robin J. Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J. Mattingly, Thomas C. Wiegers, and Zhiyong Lu. 2016. BioCreative v CDR task corpus: A resource for chemical disease relation extraction. Database: The Journal of Biological Databases and Curation, 2016.

BioRED

The BioRED data has been dedicated to the public domain. The source dataset is available at https://github.com/ncbi/BioRED.

References: Ling Luo, Po-Ting Lai, Chih-Hsuan Wei, Cecilia N Arighi, and Zhiyong Lu. 2022. BioRED: A rich biomedical relation extraction dataset. Briefing in Bioinformatics.

CANTEMIST

The CANTEMIST data originally published by A Miranda-Escalada, E Farré, and M Krallinger is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://temu.bsc.es/cantemist.

References: A Miranda-Escalada, E Farré, and M Krallinger. 2020. Named entity recognition, concept normalization and clinical coding: Overview of the cantemist track for cancer text mining in spanish, corpus, guidelines, methods and results. In Proceedings of the iberian languages evaluation forum (IberLEF 2020), CEUR workshop proceedings.

CLEANANERCorp

The CLEANANERCorp data originally published by Hend Al-Khalifa Mashael AlDuwais and Abdulmalik AlSalman is licensed under GPL 3.0. The original LICENSE file can be found in the dataset's directory. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/iwan-rg/CLEANANERCorp.

References: Hend Al-Khalifa Mashael AlDuwais and Abdulmalik AlSalman. 2024. CLEANANERCorp: Identifying and correcting incorrect labels in the ANERcorp dataset. In Proceedings of the 6th workshop on open-source arabic corpora and processing tools.

CrossNER

The CrossNER data (Copyright © 2020 Zihan Liu) was made available under the MIT license. The source dataset is available at https://github.com/zliucr/CrossNER.

References: Zihan Liu, Yan Xu, Tiezheng Yu, Wenliang Dai, Ziwei Ji, Samuel Cahyawijaya, Andrea Madotto, and Pascale Fung. 2021. CrossNER: Evaluating cross-domain named entity recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(15):13452–13460.

E-NER

The E-NER data originally published by Ting Wai Terence Au, Vasileios Lampos, and Ingemar Cox is licensed under CC BY-NC-SA 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/terenceau1/E-NER-Dataset.

References: Ting Wai Terence Au, Vasileios Lampos, and Ingemar Cox. 2022. E-NER — an annotated named entity recognition corpus of legal text. In Proceedings of the natural legal language processing workshop 2022, pages 246–255, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.

FabNER

The FabNER data originally published by Aman Kumar and Binil Starly is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/aman31kmr/fabNER.

References: Aman Kumar and Binil Starly. 2021. “FabNER”: Information extraction from manufacturing process science domain literature using named entity recognition. Journal of Intelligent Manufacturing, 33:2393–2407.

Few-NERD

The Few-NERD data originally published by Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, and Zhiyuan Liu is licensed under CC BY-SA 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/thunlp/Few-NERD.

References: Ning Ding, Guangwei Xu, Yulin Chen, Xiaobin Wang, Xu Han, Pengjun Xie, Haitao Zheng, and Zhiyuan Liu. 2021. Few-NERD: A few-shot named entity recognition dataset. In 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), pages 3198–3213, Online. Association for Computational Linguistics.

FiNER 139

The FiNER-139 data originally published by Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and Georgios Paliouras is licensed under CC BY-SA 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/nlpaueb/finer.

References: Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and Georgios Paliouras. 2022. FiNER: Financial numeric entity recognition for XBRL tagging. In Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long papers), pages 4419–4431, Dublin, Ireland. Association for Computational Linguistics.

FiNER ORD

The FiNER-ORD data originally published by Agam Shah, Abhinav Gullapalli, Ruchit Vithani, Michael Galarnyk, and Sudheer Chava is licensed under CC BY-NC 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/gtfintechlab/FiNER-ORD.

References: Agam Shah, Abhinav Gullapalli, Ruchit Vithani, Michael Galarnyk, and Sudheer Chava. 2024. FiNER-ORD: Financial named entity recognition open research dataset. arXiv preprint arXiv:2302.11157.

FoNE

The FoNE data originally published by Vésteinn Snæbjarnarson, Annika Simonsen, Goran Glavaš, and Ivan Vulić is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://huggingface.co/datasets/vesteinn/sosialurin-faroese-ner.

References: Vésteinn Snæbjarnarson, Annika Simonsen, Goran Glavaš, and Ivan Vulić. 2023. Transfer to a low-resource language via close relatives: The case study on faroese. In Proceedings of the 24th nordic conference on computational linguistics (NoDaLiDa), Tórshavn, Faroe Islands. Linköping University Electronic Press, Sweden.

German LER

The German-LER data originally published by Elena Leitner, Georg Rehm, and Julian Moreno-Schneider is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/elenanereiss/Legal-Entity-Recognition.

References: Elena Leitner, Georg Rehm, and Julian Moreno-Schneider. 2019. Fine-grained Named Entity Recognition in Legal Documents. In Semantic systems. The power of AI and knowledge graphs. Proceedings of the 15th international conference (SEMANTiCS 2019), pages 272–287, Karlsruhe, Germany. Springer. 10/11 September 2019.

Herodotos Project NER

The Herodotos-Project-NER data originally published by Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Béatrice Joyeux-Prunel, and Marie-Catherine de Marneffe is licensed under AGPL 3.0. The original LICENSE file can be found in the datasets's directories. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/Herodotos-Project/Herodotos-Project-Latin-NER-Tagger-Annotation.

References: Alexander Erdmann, David Joseph Wrisley, Benjamin Allen, Christopher Brown, Sophie Cohen-Bodénès, Micha Elsner, Yukun Feng, Brian Joseph, Bé atrice Joyeux-Prunel, and Marie-Catherine de Marneffe. 2019. Practical, efficient, and customizable active learning for named entity recognition in the digital humanities. In Proceedings of the 2019 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 1 (long and short papers), pages 2223–2234, Minneapolis, Minnesota. Association for Computational Linguistics.

JNLPBA

The JNLBPA data is licensed under the GENIA Project License for Annotated Corpora (Corpus annotations (c) GENIA Project). See the LICENSE file in the JNLBPA directory for details. Annotations in JNLBPA are licensed under CC BY 3.0 and must be attributed as stated in the included license file. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at http://www.nactem.ac.uk/GENIA/current/Shared-tasks/JNLPBA/Train/Genia4ERtraining.tar.gz.

References: Nigel Collier, Tomoko Ohta, Yoshimasa Tsuruoka, Yuka Tateisi, and Jin-Dong Kim. 2004. Introduction to the bio-entity recognition task at JNLPBA. In Proceedings of the international joint workshop on natural language processing in biomedicine and its applications (NLPBA/ BioNLP), pages 73–78, Geneva, Switzerland. COLING.

Japanese Wikipedia

The Japanese Wikipedia NER data originally published by Stockmark Inc. is licensed under CC BY-SA 3.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/stockmarkteam/ner-wikipedia-dataset.

References: 近江崇宏. 2021. Wikipedia を用いた日本語の固有表現抽出のデータセットの構築. 言語処理学会第 27 回年次大会発表論文集:350–352.

MasakhaNER-X

The MasakhaNER-X data originally published by David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D'souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, and others is licensed under CC BY-NC 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/masakhane-io/masakhane-ner.

References: David Ifeoluwa Adelani, Jade Abbott, Graham Neubig, Daniel D’souza, Julia Kreutzer, Constantine Lignos, Chester Palen-Michel, Happy Buzaaba, Shruti Rijhwani, Sebastian Ruder, Stephen Mayhew, Israel Abebe Azime, Shamsuddeen H. Muhammad, Chris Chinenye Emezue, Joyce Nakatumba-Nabende, Perez Ogayo, Aremu Anuoluwapo, Catherine Gitau, Derguene Mbaye, et al. 2021. MasakhaNER: Named Entity Recognition for African Languages. Transactions of the Association for Computational Linguistics, 9:1116–1131.

David Adelani, Graham Neubig, Sebastian Ruder, Shruti Rijhwani, Michael Beukman, Chester Palen-Michel, Constantine Lignos, Jesujoba Alabi, Shamsuddeen Muhammad, Peter Nabende, Cheikh M. Bamba Dione, Andiswa Bukula, Rooweither Mabuya, Bonaventure F. P. Dossou, Blessing Sibanda, Happy Buzaaba, Jonathan Mukiibi, Godson Kalipe, Derguene Mbaye, et al. 2022. MasakhaNER 2.0: Africa-centric transfer learning for named entity recognition. In Proceedings of the 2022 conference on empirical methods in natural language processing, pages 4488–4508, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

Sebastian Ruder, Jonathan H. Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel A-Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana L. Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David I. Adelani, et al. 2023. XTREME-UP: A user-centric scarce-data benchmark for under-represented languages. In Findings of the association for computational linguistics: EMNLP 2023, pages 1856–1884, Singapore. Association for Computational Linguistics.

MultiCoNER

The MultiCoNER data originally published by Besnik Fetahu, Zhiyu Chen, Sudipta Kar, Oleg Rokhlenko, and Shervin Malmasi is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://huggingface.co/datasets/MultiCoNER/multiconer_v2.

References: Besnik Fetahu, Zhiyu Chen, Sudipta Kar, Oleg Rokhlenko, and Shervin Malmasi. 2023a. MultiCoNER v2: A large multilingual dataset for fine-grained and noisy named entity recognition. In Findings of the association for computational linguistics: EMNLP 2023, pages 2027–2051, Singapore. Association for Computational Linguistics.

Besnik Fetahu, Sudipta Kar, Zhiyu Chen, Oleg Rokhlenko, and Shervin Malmasi. 2023b. SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). In Proceedings of the 17th international workshop on semantic evaluation (SemEval-2023). Association for Computational Linguistics.

MultiNERd

The MultiNERd data originally published by Simone Tedeschi and Roberto Navigli is licensed under CC BY-NC-SA 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://huggingface.co/datasets/Babelscape/multinerd.

References: Simone Tedeschi and Roberto Navigli. 2022. MultiNERD: A multilingual, multi-genre and fine-grained dataset for named entity recognition (and disambiguation). In Findings of the association for computational linguistics: NAACL 2022, pages 801–812, Seattle, United States. Association for Computational Linguistics.

NCBI-Disease

The NCBI-Disease data has been dedicated to the public domain. The source dataset is available at https://www.ncbi.nlm.nih.gov/research/bionlp/Data/disease/.

References: Rezarta Islamaj Doğan, Robert Leaman, and Zhiyong Lu. 2014. NCBI disease corpus: A resource for disease name recognition and concept normalization. Journal of Biomedical Informatics, 47:1–10.

NYTK-NerKor

The NYTK-NerKor data originally published by Eszter Simon and Noémi Vadász is licensed under CC BY-SA 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/nytud/NYTK-NerKor.

References: Eszter Simon and Noémi Vadász. 2021. Introducing NYTK-NerKor, A gold standard hungarian named entity annotated corpus. In Text, speech, and dialogue - 24th international conference, TSD 2021, olomouc, czech republic, september 6-9, 2021, proceedings, volume 12848, pages

Naamapadam

The Naamapadam data was made available under the CC0 public domain dedication. The source dataset is available at https://huggingface.co/datasets/ai4bharat/naamapadam.

References: Arnav Mhaske, Harshit Kedia, Sumanth Doddapaneni, Mitesh M. Khapra, Pratyush Kumar, Rudra Murthy, and Anoop Kunchukuttan. 2023. Naamapadam: A large-scale named entity annotated data for Indic languages. In Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: Long papers), pages 10441–10456, Toronto, Canada. Association for Computational Linguistics.

RaTE-NER

The RaTE-NER data originally published by Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie is licensed under CC BY-NC 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://huggingface.co/datasets/Angelakeke/RaTE-NER.

References: Weike Zhao, Chaoyi Wu, Xiaoman Zhang, Ya Zhang, Yanfeng Wang, and Weidi Xie. 2024. RaTEScore: A metric for radiology report generation. In Proceedings of the 2024 conference on empirical methods in natural language processing, pages 15004–15019.

SciREX

The SciREX data originally published by Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, and Iz Beltagy is licensed under the Apache License, Version 2.0. The original LICENSE file can be found in the datasets's directory. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/allenai/SciREX.

References: Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, and Iz Beltagy. 2020. SciREX: A challenge dataset for document-level information extraction. In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 7506–7516, Online. Association for Computational Linguistics.

SOFC-Exp

The SOFC-Exp data originally published by Annemarie Friedrich, Heike Adel, Federico Tomazic, Johannes Hingerl, Renou Benteau, Anika Marusczyk, and Lukas Lange is licensed under CC BY-NC 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/boschresearch/sofc-exp_textmining_resources.

References: Annemarie Friedrich, Heike Adel, Federico Tomazic, Johannes Hingerl, Renou Benteau, Anika Marusczyk, and Lukas Lange. 2020. The SOFC-Exp corpus and neural approaches to information extraction in the materials science domain. In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 1255–1268, Online. Association for Computational Linguistics.

SciER

The SciER data originally published by Qi Zhang, Zhijia Chen, Huitong Pan, Cornelia Caragea, Longin Jan Latecki, and Eduard Dragut is licensed under GPL 3.0. The original LICENSE file can be found in the datasets's directories. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/edzq/SciER.

References: Qi Zhang, Zhijia Chen, Huitong Pan, Cornelia Caragea, Longin Jan Latecki, and Eduard Dragut. 2024. SciER: An entity and relation extraction dataset for datasets, methods, and tasks in scientific documents. In Proceedings of the 2024 conference on empirical methods in natural language processing, pages 13083–13100, Miami, Florida, USA. Association for Computational Linguistics.

SoMeSci

The SoMeSci data originally published by David Schindler, Felix Bensmann, Stefan Dietze, and Frank Krüger is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/dave-s477/SoMeSci.

References: David Schindler, Felix Bensmann, Stefan Dietze, and Frank Krü ger. 2021. SoMeSci- a 5 star open data gold standard knowledge graph of software mentions in scientific articles. In Proceedings of the 30th ACM international conference on information & knowledge management, pages 4574–4583, New York, NY, USA. Association for Computing Machinery.

David Schindler, Tazin Hossain, Sascha Spors, and Frank Krüger. 2024. A multilevel analysis of data quality for formal software citation. Quantitative Science Studies, 5(3):637–667.

StackOverflowNER

The StackOverflowNER data (Copyright © 2020 JeniyaTabassum) was made available under the MIT license. The source dataset is available at https://github.com/jeniyat/StackOverflowNER.

References: Jeniya Tabassum, Mounica Maddela, Wei Xu, and Alan Ritter. 2020. Code and named entity recognition in StackOverflow. In Proceedings of the 58th annual meeting of the association for computational linguistics, pages 4913–4926, Online. Association for Computational Linguistics.

TASTEset

The TASTEset data (Copyright © 2022 taisti) was made available under the MIT license. The source dataset is available at https://github.com/taisti/tasteset.

References: Agnieszka Lawrynowicz, Anna Wróblewska, Agnieszka Kaliska, Maciej Pawlowski, Dawid Wiśniewski, Witold Sosnowski, and Jakub Dutkiewicz. 2023. Fine-grained and complex food entity recognition benchmark for ingredient substitution. In Proceedings of the 12th knowledge capture conference 2023, pages 25–29, New York, NY, USA. Association for Computing Machinery.

Thai NER

The Thai NER 2.2 data originally published by Wannaphong Phatthiyaphaibun is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://zenodo.org/records/10795907.

References: Wannaphong Phatthiyaphaibun. 2024. Thai NER 2.2.

Turku NER corpus

The Turku NER corpus originally published by Jouni Luoma, Miika Oinonen, Maria Pyykönen, Veronika Laippala, and Sampo Pyysalo is licensed under CC BY-SA 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/TurkuNLP/turku-ner-corpus.

References: Jouni Luoma, Miika Oinonen, Maria Pyykönen, Veronika Laippala, and Sampo Pyysalo. 2020. A broad-coverage corpus for Finnish named entity recognition. In Proceedings of the 12th language resources and evaluation conference, pages 4615–4624.

Tweebank-NER

The Tweebank-NER data originally published by Hang Jiang, Yining Hua, Doug Beeferman, and Deb Roy is licensed under the Apache License, Version 2.0. The original LICENSE file can be found in the datasets's directory. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/mit-ccc/TweebankNLP.

References: Hang Jiang, Yining Hua, Doug Beeferman, and Deb Roy. 2022. Annotating the tweebank corpus on named entity recognition and building NLP models for social media analysis. In Proceedings of the thirteenth language resources and evaluation conference, pages 7199–7208, Marseille, France. European Language Resources Association.

UniversalNER

The Danish-DDT (da_ddt), Serbian-SET (sr_set), Norwegian-NDT (nob_norne and nno_norne), Slovak-SNK (sk_snk), Croatian-SET (hr_set), Cebuano-GJA (ceb_gja), Chinese-GSD (zh_gsd), Chinese-GSDSIMP (zh_gsdsimp), English-EWT (en_ewt), Maghrebi_Arabic_French-Arabizi (qaf_arabizi), Portuguese-Bosque (pt_bosque), Swedish-PUD (sv_pud), Swedish-Talbanken (sv_talbanken), and Tagalog-TRG (tl_trg) subsets are licensed under CC BY-SA 4.0.

The Chinese-PUD (zh_pud), English-PUD (en_pud), German-PUD (de_pud), Portuguese-PUD (pt_pud), and Russian-PUD (ru_pud) subsets are licensed under CC BY-SA 3.0.

The Tagalog-Ugnayan (tl_ugnayan) subset is licensed under CC BY-NC-SA 4.0.

All subsets have been processed from their original source and converted to the MELD format. The corresponding source datasets for each subset can be found under https://github.com/UniversalNER.

UniversalNER was originally published by Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Šuppa, Hila Gonen, Joseph Marvin Imperial, Börje F. Karlsson, Peiqin Lin, Nikola Ljubešić, LJ Miranda, Barbara Plank, Arij Riab, and Yuval Pinter. The Norwegian-NDT dataset was derived from the work of Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal. The Danish-DDT dataset was derived from the work of Barbara Plank, Kristian Nørgaard Jensen, and Rob van der Goot. See the references below for further details.

References: Fredrik Jørgensen, Tobias Aasmoe, Anne-Stine Ruud Husevåg, Lilja Øvrelid, and Erik Velldal. 2020. NorNE: Annotating named entities for Norwegian. In Proceedings of the twelfth language resources and evaluation conference, pages 4547–4556, Marseille, France. European Language Resources Association.

Stephen Mayhew, Terra Blevins, Shuheng Liu, Marek Šuppa, Hila Gonen, Joseph Marvin Imperial, Börje F. Karlsson, Peiqin Lin, Nikola Ljubešić, LJ Miranda, Barbara Plank, Arij Riab, and Yuval Pinter. 2024. Universal NER: A gold-standard multilingual named entity recognition benchmark. In Proceedings of the 2024 conference of the north american chapter of the association for computational linguistics (NAACL).

Barbara Plank, Kristian Nørgaard Jensen, and Rob van der Goot. 2020. DaN+: Danish nested named entities and lexical normalization. In Proceedings of the 28th international conference on computational linguistics, pages 6649–6662, Barcelona, Spain (Online). International Committee on Computational Linguistics.

WIESP2022

The WIESP2022 data originally published by Felix Grezes, Sergi Blanco-Cuaresma, Thomas Allen, and Tirthankar Ghosal is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://huggingface.co/datasets/adsabs/WIESP2022-NER.

References: Felix Grezes, Sergi Blanco-Cuaresma, Thomas Allen, and Tirthankar Ghosal. 2022. Overview of the first shared task on detecting entities in the astrophysics literature (DEAL). In Proceedings of the first workshop on information extraction from scientific publications, pages 1–7, Online. Association for Computational Linguistics.

WLP

The WLP data (Copyright © 2018 chaitanya2334) was made available under the MIT license. The source dataset is available at https://github.com/chaitanya2334/WLP-Dataset.

References: Chaitanya Kulkarni, Wei Xu, Alan Ritter, and Raghu Machiraju. 2018. An annotated corpus for machine reading of instructions in wet lab protocols. In Proceedings of the 2018 conference of the north American chapter of the association for computational linguistics: Human language technologies, volume 2 (short papers), pages 97–106, New Orleans, Louisiana. Association for Computational Linguistics.

WNUT2017

The WNUT2017 data originally published by Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham is licensed under CC BY 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/leondz/emerging_entities_17.

References: Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017. Results of the WNUT2017 shared task on novel and emerging entity recognition. In Proceedings of the 3rd workshop on noisy user-generated text, pages 140–147, Copenhagen, Denmark. Association for Computational Linguistics.

Weibo NER

The Weibo NER corpus originally published by Nanyun Peng and Mark Dredze, with revisions by Hangfeng He, is licensed under CC BY-SA 3.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://github.com/hltcoe/golden-horse.

References: Hangfeng He and Xu Sun. 2017. F-score driven max margin neural network for named entity recognition in Chinese social media. In Proceedings of the 15th conference of the European chapter of the association for computational linguistics: Volume 2, short papers, pages 713–718, Valencia, Spain. Association for Computational Linguistics.

Nanyun Peng and Mark Dredze. 2015. Named entity recognition for chinese social media with jointly trained embeddings. In Processings of the conference on empirical methods in natural language processing (EMNLP), pages 548--554.

Nanyun Peng and Mark Dredze. 2016. Improving named entity recognition for chinese social media with word segmentation representation learning. In Proceedings of the 54th annual meeting of the association for computational linguistics (ACL), volume 2, pages 149–155.

WikiNEuRal

The WikiNEuRal data originally published by Simone Tedeschi, Valentino Maiorca, Niccolò Campolungo, Francesco Cecconi, and Roberto Navigli is licensed under CC BY-NC-SA 4.0. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://huggingface.co/datasets/Babelscape/wikineural.

References: Simone Tedeschi, Valentino Maiorca, Niccolò Campolungo, Francesco Cecconi, and Roberto Navigli. 2021. WikiNEuRal: Combined neural and knowledge-based silver data creation for multilingual NER. In Findings of the association for computational linguistics: EMNLP 2021, pages 2521–2533, Punta Cana, Dominican Republic. Association for Computational Linguistics.

idner news 2k

The idner news 2k (Copyright © 2020 khairunnisaor) was made available under the MIT license. The source dataset is available at https://github.com/khairunnisaor/idner-news-2k.

References: Siti Oryza Khairunnisa, Aizhan Imankulova, and Mamoru Komachi. 2020. Towards a standardized dataset on indonesian named entity recognition. In Proceedings of the 1st conference of the asia-pacific chapter of the association for computational linguistics and the 10th international joint conference on natural language processing: Student research workshop.

pioNER

The pioNER data originally published by Tsolak Ghukasyan, Garnik Davtyan, K. Avetisyan, and Ivan Andrianov is licensed under the Apache License, Version 2.0. The original LICENSE file can be found in the datasets's directory. The dataset has been processed from its original source and converted to the MELD format. The source dataset is available at https://huggingface.co/datasets/Karavet/pioNER-Armenian-Named-Entity.

References: Tsolak Ghukasyan, Garnik Davtyan, K. Avetisyan, and Ivan Andrianov. 2018. pioNER: Datasets and baselines for armenian named entity recognition. 2018 Ivannikov Ispras Open Conference (ISPRAS):56–61.