| dc.contributor.author | Novotný, Vít |
| dc.contributor.author | Luger, Kristýna |
| dc.contributor.author | Štefánik, Michal |
| dc.contributor.author | Vrabcová, Tereza |
| dc.contributor.author | Horák, Aleš |
| dc.date.accessioned | 2023-01-23T20:43:53Z |
| dc.date.available | 2023-01-23T20:43:53Z |
| dc.date.issued | 2022-11-30 |
| dc.identifier.uri | http://hdl.handle.net/11234/1-5024 |
| dc.description | This is an open dataset of sentences from 19th and 20th century letterpress reprints of documents from the Hussite era. The dataset contains a corpus for language modeling and human annotations for named entity recognition (NER). |
| dc.language.iso | ces |
| dc.language.iso | eng |
| dc.language.iso | deu |
| dc.language.iso | lat |
| dc.publisher | Masaryk University, Brno |
| dc.relation.isreferencedby | https://nlp.fi.muni.cz/projects/ahisto/ner-dataset |
| dc.relation.replaces | http://hdl.handle.net/11234/1-4936 |
| dc.rights | Public Domain Dedication (CC Zero) |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ |
| dc.source.uri | https://starfos.tacr.cz/en/project/TL03000365 |
| dc.subject | NER |
| dc.subject | named entity recognition |
| dc.subject | Medieval |
| dc.title | A Human-Annotated Dataset for Language Modeling and Named Entity Recognition in Medieval Documents (2023-01-05) |
| dc.type | corpus |
| metashare.ResourceInfo#ContentInfo.mediaType | text |
| dc.rights.label | PUB |
| has.files | yes |
| branding | LINDAT / CLARIAH-CZ |
| contact.person | Vít Novotný witiko@mail.muni.cz Masaryk University, Brno |
| sponsor | TAČR TL03000365 Accessible historical sources. Making medieval written documents available in the form of a contextual database nationalFunds |
| sponsor | Ministerstvo školství, mládeže a tělovýchovy České republiky LM2018101 LINDAT/CLARIAH-CZ: Digitální výzkumná infrastruktura pro jazykové technologie, umění a humanitní vědy nationalFunds |
| size.info | 205 files |
| size.info | 2.89 gb |
| files.size | 3096251769 |
| files.count | 3 |
Files in this item
- Name
- language-modeling-corpus.zip
- Size
- 633.79 MB
- Format
- application/zip
- Description
- Sentences for unsupervised training and validation of language models
- MD5
- b6ed0a8e7dc263d1a1e635d6d5770f6b
- dataset_mlm_non-crossing_only-relevant_training.txt6 MB
- dataset_mlm_non-crossing_all_validation.txt64 MB
- dataset_mlm_all_only-relevant_validation.txt719 kB
- dataset_mlm_all_only-relevant_training.txt7 MB
- dataset_mlm_non-crossing_all_training.txt499 MB
- dataset_mlm_all_all_validation.txt78 MB
- dataset_mlm_non-crossing_only-relevant_validation.txt536 kB
- dataset_mlm_all_all_training.txt601 MB
- Name
- named-entity-recognition-annotations-small.zip
- Size
- 978.29 MB
- Format
- application/zip
- Description
- Sentences and NER tags that we used for the supervised training, validation, and testing of intermediate language models
- MD5
- 92d80ec8d6e66263295797b1e81bd60d
- dataset_ner_manatee_non-crossing_only-relevant_testing_001-400.ner_tags.txt41 kB
- dataset_ner_manatee+regests_all_only-relevant_validation_automatically_tagged.sentences.txt628 kB
- dataset_ner_fuzzy-regex+regests_non-crossing_all_validation_automatically_tagged.ner_tags.txt10 MB
- dataset_ner_fuzzy-regex+regests_non-crossing_only-relevant_training_automatically_tagged.sentences.txt3 MB
- dataset_ner_regests_testing.sentences.txt186 kB
- dataset_ner_fuzzy-regex+regests_all_all_training.ner_tags.txt50 MB
- dataset_ner_manatee_non-crossing_all_validation.ner_tags.txt2 MB
- dataset_ner_fuzzy-regex+regests_non-crossing_only-relevant_validation_automatically_tagged.ner_tags.txt317 kB
- dataset_ner_fuzzy-regex_non-crossing_all_validation_automatically_tagged.ner_tags.txt10 MB
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- dataset_ner_manatee_non-crossing_only-relevant_training_automatically_tagged.sentences.txt1 MB
- dataset_ner_fuzzy-regex+regests_all_only-relevant_training.sentences.txt4 MB
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- Name
- named-entity-recognition-annotations-large.zip
- Size
- 1.31 GB
- Format
- application/zip
- Description
- Sentences and NER tags for supervised training, validation, and testing of language models
- MD5
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