Změny mezi verzí 7 a verzí 8 u NerDataset
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- 28. 11. 2022 16:35:54 (před 20 měsíci)
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NerDataset
v7 v8 9 9 * 8 files named `dataset_mlm_*.txt` that contain sentences for unsupervised training of language models.[[BR]]We used the following three variables to produce the different files: 10 10 1. The sentences are extracted from book OCR texts and may therefore span several pages.[[BR]]However, page boundaries contain pollutants such as running heads, footnotes, and page numbers.[[BR]]We either allow the sentences to cross page boundaries (`all`) or not (`non-crossing`). 11 1. The sentences come from all book pages (`all`) or just those considered relevant by expertannotators (`only-relevant`).11 1. The sentences come from all book pages (`all`) or just those considered relevant by human annotators (`only-relevant`). 12 12 1. We split the sentences roughly into 90% for training (`training`) and 10% for validation (`validation`). 13 13 * 16 tuples of files named `dataset_ner_*.sentences.txt`, `.ner_tags.txt`, and in two cases also `.docx`.[[BR]]These contain sentences and NER tags for supervised training, validation, and testing of language models.[[BR]]The `.docx` files are authored by human annotators and may contain extra details missing from files `.sentences.txt` and `.ner_tags.txt`.[[BR]]Here are the five variables that we used to produce the different files: 14 14 1. The sentences may originate from book OCR texts using information retrieval techniques (`fuzzy-regex` or `manatee`).[[BR]]The sentences may also originate from regests (`regests`) or both books and regests (`fuzzy-regex+regests` and `fuzzy-regex+manatee`). 15 15 1. When sentences originate from book OCR texts, they may span several pages of a book.[[BR]]However, page boundaries contain pollutants such as running heads, footnotes, and page numbers.[[BR]]We either allow the sentences to cross page boundaries (`all`) or not (`non-crossing`). 16 1. When sentences originate from book OCR texts, they may come from book pages of different relevance.[[BR]]We either use sentences from all book pages (`all`) or just those considered relevant by expertannotators (`only-relevant`).16 1. When sentences originate from book OCR texts, they may come from book pages of different relevance.[[BR]]We either use sentences from all book pages (`all`) or just those considered relevant by human annotators (`only-relevant`). 17 17 1. When sentences and NER tags originate from book OCR texts using information retrieval techniques, many entities in the sentences may lack tags.[[BR]]Therefore, we also provide NER tags completed by language models (`automatically_tagged`) and human annotators (`tagged`). 18 18 1. We split the sentences roughly into 80% for training (`training`), 10% for validation (`validation`), and 10% for testing (`testing`).[[BR]]For repeated testing, we subdivide the testing split (`testing_001-400` and `testing_401-500`).