--- dataset_info: - config_name: dan features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 280976 num_examples: 719 - name: test num_bytes: 64073 num_examples: 181 download_size: 164904 dataset_size: 345049 - config_name: deu features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 272039 num_examples: 1628 - name: test num_bytes: 92741 num_examples: 583 - name: validation num_bytes: 89123 num_examples: 573 download_size: 217092 dataset_size: 453903 - config_name: eng features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 631212 num_examples: 2360 - name: test num_bytes: 526979 num_examples: 1967 - name: validation num_bytes: 164519 num_examples: 590 download_size: 677203 dataset_size: 1322710 - config_name: hrv features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 971981 num_examples: 4760 - name: test num_bytes: 276506 num_examples: 1586 - name: validation num_bytes: 342128 num_examples: 1588 download_size: 812715 dataset_size: 1590615 - config_name: ind features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 697988 num_examples: 3016 - name: test num_bytes: 179636 num_examples: 861 - name: validation num_bytes: 85344 num_examples: 431 download_size: 435304 dataset_size: 962968 - config_name: ind_eng features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 247175 num_examples: 495 - name: test num_bytes: 76432 num_examples: 165 - name: validation num_bytes: 84002 num_examples: 165 download_size: 210136 dataset_size: 407609 - config_name: ita features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 228436 num_examples: 593 - name: test num_bytes: 36353 num_examples: 100 download_size: 138740 dataset_size: 264789 - config_name: jpn features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 1155890 num_examples: 2132 - name: test num_bytes: 422488 num_examples: 609 - name: validation num_bytes: 202539 num_examples: 305 download_size: 567677 dataset_size: 1780917 - config_name: kor features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 340854 num_examples: 1701 - name: test num_bytes: 40615 num_examples: 214 - name: validation num_bytes: 49262 num_examples: 212 download_size: 269680 dataset_size: 430731 - config_name: nld features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 218649 num_examples: 907 - name: test num_bytes: 92867 num_examples: 308 - name: validation num_bytes: 69557 num_examples: 308 download_size: 206708 dataset_size: 381073 - config_name: rus features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 274896 num_examples: 1544 - name: test num_bytes: 95267 num_examples: 386 download_size: 174399 dataset_size: 370163 - config_name: slv features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 768742 num_examples: 4670 - name: test num_bytes: 256900 num_examples: 1557 - name: validation num_bytes: 261687 num_examples: 1557 download_size: 631982 dataset_size: 1287329 - config_name: spa features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 131515 num_examples: 568 - name: test num_bytes: 125214 num_examples: 531 download_size: 145378 dataset_size: 256729 - config_name: srp features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 967757 num_examples: 4138 - name: test num_bytes: 295265 num_examples: 1377 - name: validation num_bytes: 305680 num_examples: 1379 download_size: 753207 dataset_size: 1568702 - config_name: tha features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 3814903 num_examples: 1750 - name: test num_bytes: 1095005 num_examples: 500 - name: validation num_bytes: 544527 num_examples: 250 download_size: 1166871 dataset_size: 5454435 - config_name: tur features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 141822 num_examples: 570 - name: test num_bytes: 36070 num_examples: 143 download_size: 125137 dataset_size: 177892 - config_name: tur_deu features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 255826 num_examples: 800 - name: test num_bytes: 70352 num_examples: 229 download_size: 180596 dataset_size: 326178 - config_name: vie features: - name: orig list: string - name: norm list: string splits: - name: train num_bytes: 1793840 num_examples: 8371 - name: test num_bytes: 232579 num_examples: 1045 - name: validation num_bytes: 240371 num_examples: 1050 download_size: 645576 dataset_size: 2266790 configs: - config_name: dan data_files: - split: train path: dan/train-* - split: test path: dan/test-* - config_name: deu data_files: - split: train path: deu/train-* - split: test path: deu/test-* - split: validation path: deu/validation-* - config_name: eng data_files: - split: train path: eng/train-* - split: test path: eng/test-* - split: validation path: eng/validation-* - config_name: hrv data_files: - split: train path: hrv/train-* - split: test path: hrv/test-* - split: validation path: hrv/validation-* - config_name: ind data_files: - split: train path: ind/train-* - split: test path: ind/test-* - split: validation path: ind/validation-* - config_name: ind_eng data_files: - split: train path: ind_eng/train-* - split: test path: ind_eng/test-* - split: validation path: ind_eng/validation-* - config_name: ita data_files: - split: train path: ita/train-* - split: test path: ita/test-* - config_name: jpn data_files: - split: train path: jpn/train-* - split: test path: jpn/test-* - split: validation path: jpn/validation-* - config_name: kor data_files: - split: train path: kor/train-* - split: test path: kor/test-* - split: validation path: kor/validation-* - config_name: nld data_files: - split: train path: nld/train-* - split: test path: nld/test-* - split: validation path: nld/validation-* - config_name: rus data_files: - split: train path: rus/train-* - split: test path: rus/test-* - config_name: slv data_files: - split: train path: slv/train-* - split: test path: slv/test-* - split: validation path: slv/validation-* - config_name: spa data_files: - split: train path: spa/train-* - split: test path: spa/test-* - config_name: srp data_files: - split: train path: srp/train-* - split: test path: srp/test-* - split: validation path: srp/validation-* - config_name: tha data_files: - split: train path: tha/train-* - split: test path: tha/test-* - split: validation path: tha/validation-* - config_name: tur data_files: - split: train path: tur/train-* - split: test path: tur/test-* - config_name: tur_deu data_files: - split: train path: tur_deu/train-* - split: test path: tur_deu/test-* - config_name: vie data_files: - split: train path: vie/train-* - split: test path: vie/test-* - split: validation path: vie/validation-* language: - da - de - en - hr - id - ja - ko - nl - ru - sl - es - sr - th - tr - vi --- ## Multilingual Lexical Normalization [![MultiLexNorm](https://bytebucket.org/robvanderg/multilexnorm/raw/4345a4badba4d93515e016118483428d535add9c/multilexnorm-cover.png)]() This is the HuggingFace version of the MultiLexNorm dataset. The original repository (https://bitbucket.org/robvanderg/multilexnorm/) contains all data pre-processed in the same format: ``` Most most social social pple people r are troublesome troublesome ``` Some of the languages include annotation for word splits and merges. When a word is split, the normalization column include a white-space character, and with a merge the normalization is only included for the first word: ``` if If i i have have a a head headache ache tomorro tomorrow ima i'm going to be be pissed pissed ``` This Huggingface version instead can be loaded like: ``` from datasets import load_dataset ds = load_dataset( "robvanderg/MultiLexNorm", "dan", split="train", ) print(ds) Dataset({ features: ['orig', 'norm'], num_rows: 719 }) ``` The internal format is the same, so the norm list might contain empty elements or whitespaces (whereas the orig shouldn't) ## Citation The data in this benchmark comes from many different sources, we collected existing datasets and converged them to the same formate. This started with the original MultiLexNorm, and did the same for MultiLexNorm++. We added Russian data later. For the original 12 language variants: ``` @inproceedings{multilexnorm, title= {{MultiLexNorm}: A Shared Task on Multilingual Lexical Normalization}, author = "van der Goot, Rob and Ramponi, Alan and Zubiaga, Arkaitz and Plank, Barbara and Muller, Benjamin and San Vicente Roncal, I\~{n}aki and Ljube\v{s}i\'{c}, Nikola and \c{C}etino\u{g}lu, {\"O}zlem and Mahendra, Rahmad and \c{C}olako\u{g}lu, Talha and Baldwin, Timothy and Caselli, Tommaso and Sidorenko, Wladimir", booktitle = "Proceedings of the 7th Workshop on Noisy User-generated Text (W-NUT 2021)", year = "2021", publisher = "Association for Computational Linguistics", address = "Punta Cana, Dominican Republic" } ``` For the additional five Asian languages in MultiLexNorm++: ``` @article{10.1145/3812651, author = {Buaphet, Weerayut and Nguyen, Thanh-Nhi and Kondo, Risa and Kajiwara, Tomoyuki and Kim, Yumin and Lee, Jimin and Lee, Hwanhee and Lovenia, Holy and Limkonchotiwat, Peerat and Nutanong, Sarana and Van der Goot, Rob}, title = {MultiLexNorm++: A Unified Benchmark and a Generative Model for Lexical Normalization for Asian Languages}, year = {2026}, issue_date = {July 2026}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, volume = {25}, number = {7}, issn = {2375-4699}, url = {https://doi.org/10.1145/3812651}, doi = {10.1145/3812651}, journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.}, month = jul, articleno = {61}, numpages = {14}, } ``` For Russian: ``` @InProceedings{10.1007/978-3-032-04958-2_13, author="Feoktistov, Grigorii and Morozov, Dmitry", editor="Bakaev, Maxim and Bolgov, Radomir and Chizhik, Anna and Chugunov, Andrei and Demareva, Valeriia and Kabanov, Yury and Pereira, Roberto and R., Elakkiya and Zhang, Wei", title="TL; DR: Text Normalization for Social Media Corpus", booktitle="Internet and Modern Society", year="2026", publisher="Springer Nature Switzerland", address="Cham", pages="168--176", } ``` We would also strongly encourage you to cite the original papers if you use this data: ``` We use the MultiLexNorm dataset~\cite{multilexnorm,10.1145/3812651,10.1007/978-3-032-04958-2_13,plank-etal-2020-dan,sidarenka2013rule,baldwin-etal-2015-shared,alegria2013introduccion,11356/1170,barik-etal-2019-normalization,van-der-goot-etal-2020-norm,dutchNorm,11356/1123,11356/1171,colakoglu-etal-2019-normalizing,van-der-goot-etal-2021-lexical} ``` The remaining bib files are listed below: ``` @inproceedings{plank-etal-2020-dan, title = "{D}a{N}+: {D}anish Nested Named Entities and Lexical Normalization", author = "Plank, Barbara and Jensen, Kristian N{\o}rgaard and van der Goot, Rob", booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "International Committee on Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.coling-main.583", doi = "10.18653/v1/2020.coling-main.583", pages = "6649--6662", } @inproceedings{sidarenka2013rule, title={Rule-based normalization of {G}erman {T}witter messages}, author={Sidarenka, Uladzimir and Scheffler, Tatjana and Stede, Manfred}, booktitle={Proc. of the GSCL Workshop Verarbeitung und Annotation von Sprachdaten aus Genres internetbasierter Kommunikation}, year={2013} } @inproceedings{baldwin-etal-2015-shared, title = "Shared Tasks of the 2015 Workshop on Noisy User-generated Text: {T}witter Lexical Normalization and Named Entity Recognition", author = "Baldwin, Timothy and de Marneffe, Marie Catherine and Han, Bo and Kim, Young-Bum and Ritter, Alan and Xu, Wei", booktitle = "Proceedings of the Workshop on Noisy User-generated Text", month = jul, year = "2015", address = "Beijing, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/W15-4319", doi = "10.18653/v1/W15-4319", pages = "126--135", } @inproceedings{alegria2013introduccion, title={Introducci{\'o}n a la Tarea Compartida {Tweet-Norm} 2013: Normalizaci{\'o}n L{\'e}xica de Tuits en {E}spa{\~n}ol.}, author={Alegria, Inaki and Aranberri, Nora and Fresno, V{\'\i}ctor and Gamallo, Pablo and Padr{\'o}, Lluis and San Vicente, Inaki and Turmo, Jordi and Zubiaga, Arkaitz}, booktitle={Tweet-Norm@ SEPLN}, pages={1--9}, year={2013} } @misc{11356/1170, title = {Croatian {T}witter training corpus {ReLDI}-{NormTagNER}-hr 2.0}, author = {Ljube{\v s}i{\'c}, Nikola and Erjavec, Toma{\v z} and Mili{\v c}evi{\'c}, Maja and Samard{\v z}i{\'c}, Tanja}, url = {http://hdl.handle.net/11356/1170}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {Creative Commons - Attribution 4.0 International ({CC} {BY} 4.0)}, year = {2017} } @inproceedings{barik-etal-2019-normalization, title = "Normalization of {I}ndonesian-{E}nglish Code-Mixed {T}witter Data", author = "Barik, Anab Maulana and Mahendra, Rahmad and Adriani, Mirna", booktitle = "Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-5554", doi = "10.18653/v1/D19-5554", pages = "417--424", } @inproceedings{van-der-goot-etal-2020-norm, title = "Norm It! Lexical Normalization for {I}talian and Its Downstream Effects for Dependency Parsing", author = "van der Goot, Rob and Ramponi, Alan and Caselli, Tommaso and Cafagna, Michele and De Mattei, Lorenzo", booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://www.aclweb.org/anthology/2020.lrec-1.769", pages = "6272--6278", language = "English", ISBN = "979-10-95546-34-4", } @mastersthesis{dutchNorm, author = {Schuur, Youri}, title = {Normalization for {D}utch for improved POS tagging}, school = {University of Groningen}, year = 2020, url = {http://robvanderg.github.io/doc/normalization_dutch.pdf} } @misc{11356/1123, title = {{CMC} training corpus {Janes-Tag} 2.0}, author = {Erjavec, Toma{\v z} and Fi{\v s}er, Darja and {\v C}ibej, Jaka and Arhar Holdt, {\v S}pela and Ljube{\v s}i{\'c}, Nikola and Zupan, Katja}, url = {http://hdl.handle.net/11356/1123}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)}, year = {2017} } @misc{11356/1171, title = {Serbian {T}witter training corpus {ReLDI}-{NormTagNER}-sr 2.0}, author = {Ljube{\v s}i{\'c}, Nikola and Erjavec, Toma{\v z} and Mili{\v c}evi{\'c}, Maja and Samard{\v z}i{\'c}, Tanja}, url = {http://hdl.handle.net/11356/1171}, note = {Slovenian language resource repository {CLARIN}.{SI}}, copyright = {Creative Commons - Attribution 4.0 International ({CC} {BY} 4.0)}, year = {2017} } @inproceedings{colakoglu-etal-2019-normalizing, title = "Normalizing Non-canonical {T}urkish Texts Using Machine Translation Approaches", author = {{\c{C}}olako{\u{g}}lu, Talha and Sulubacak, Umut and Tantu{\u{g}}, Ahmet C{\"u}neyd}, booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop", month = jul, year = "2019", address = "Florence, Italy", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/P19-2037", doi = "10.18653/v1/P19-2037", pages = "267--272" } @inproceedings{van-der-goot-etal-2021-lexical, title = "Lexical Normalization for Code-switched Data and its Effect on {POS} Tagging", author = "van der Goot, Rob and \c{C}etino{\u{g}}lu, {\"O}zlem", booktitle = "Proceedings of the 16th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers", month = feb, year = "2021", publisher = "Association for Computational Linguistics" } ```