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--- |
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license: cc-by-4.0 |
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dataset_info: |
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features: |
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- name: original |
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dtype: string |
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- name: tokens |
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sequence: string |
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- name: labels |
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sequence: string |
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- name: qid |
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sequence: string |
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- name: language |
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dtype: string |
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- name: url |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 5669993 |
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num_examples: 6764 |
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download_size: 1906917 |
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dataset_size: 5669993 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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language: |
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- nl |
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- en |
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- es |
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- pt |
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- el |
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- fr |
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- de |
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pretty_name: winnl |
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task_categories: |
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- token-classification |
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--- |
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# WiNNL |
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WikiNews Named entity recognition and Linking (WiNNL) is a multilingual news NER & NEL benchmark based on Wikinews articles. |
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The dataset was created by automatically scraping and tagging news articles, and manually corrected by native speakers to ensure accuracy. |
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You can find more information in the paper: |
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https://aclanthology.org/2024.dlnld-1.3.pdf |
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The dataset includes the following NER classes in IOB format (`labels`): |
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* **PER** (Person): person names |
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* **LOC** (Location): geographical locations |
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* **ORG** (Organisation): organisations |
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* **AMB** (Ambiguous): entities that had an ambigous wikidata link in the article, and could be classified as multiple NER classes |
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* **DATE** (Date): dates (e.g. "2022-01-01", "5th of January 2022") |
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* **DISEASE** (Disease): diseases (e.g. "cancer", "COVID-19") |
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* **EVT** (Event): events (e.g. "2024 US elections") |
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* **SPE** (Sport Event): sports events (e.g. "World Cup", "Olympics") |
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* **OTH** (Other): other entities that do not fit into any of the above categories |
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***Please note that only the PER, ORG and LOC classes have been corrected manually.*** |
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The `qid` column contains the Wikidata entity identifiers for the entities in the dataset, also in IOB format. |
