{
 "id": "SEACrowd/id_wsd",
 "owner": "SEACrowd",
 "name": "id_wsd",
 "downloads": 88,
 "likes": 0,
 "last_modified": "2024-06-24T13:32:28.000Z",
 "stated_licence": null,
 "stated_from": "none",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [
  "LICENSE"
 ],
 "sources": [],
 "gated": false,
 "tasks": [
  "word-sense-disambiguation"
 ],
 "citation": "@inproceedings{mahendra-etal-2018-cross,\n    title = \"Cross-Lingual and Supervised Learning Approach for {I}ndonesian Word Sense Disambiguation Task\",\n    author = \"Mahendra, Rahmad  and\n      Septiantri, Heninggar  and\n      Wibowo, Haryo Akbarianto  and\n      Manurung, Ruli  and\n      Adriani, Mirna\",\n    booktitle = \"Proceedings of the 9th Global Wordnet Conference\",\n    month = jan,\n    year = \"2018\",\n    address = \"Nanyang Technological University (NTU), Singapore\",\n    publisher = \"Global Wordnet Association\",\n    url = \"https://aclanthology.org/2018.gwc-1.28\",\n    pages = \"245--250\",\n    abstract = \"Ambiguity is a problem we frequently face in Natural Language Processing. Word Sense Disambiguation (WSD) is a task to determine the correct sense of an ambiguous word. However, research in WSD for Indonesian is still rare to find. The availability of English-Indonesian parallel corpora and WordNet for both languages can be used as training data for WSD by applying Cross-Lingual WSD method. This training data is used as an input to build a model using supervised machine learning algorithms. Our research also examines the use of Word Embedding features to build the WSD model.\",\n}\n\n\n@article{lovenia2024seacrowd,\n    title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages}, \n    author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah an",
 "signal": "unclear",
 "reason": "The checks could not identify a recognised licence ID in the dataset's metadata.",
 "revision": "7a43c4ac980a42910baf0cbc5f7d3fb753e2c7a1",
 "stated_evidence": "https://huggingface.co/datasets/SEACrowd/id_wsd/blob/7a43c4ac980a42910baf0cbc5f7d3fb753e2c7a1/README.md",
 "file_evidence": "https://huggingface.co/datasets/SEACrowd/id_wsd/blob/7a43c4ac980a42910baf0cbc5f7d3fb753e2c7a1/LICENSE",
 "card_nc": false
}