Datasets:

leondz
/

wnut_17

- {"wnut_17": {"description": "WNUT 17: Emerging and Rare entity recognition\n\nThis shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.\nNamed entities form the basis of many modern approaches to other tasks (like event clustering and summarisation),\nbut recall on them is a real problem in noisy text - even among annotators. This drop tends to be due to novel entities and surface forms.\nTake for example the tweet \u201cso.. kktny in 30 mins?\u201d - even human experts find entity kktny hard to detect and resolve.\nThis task will evaluate the ability to detect and classify novel, emerging, singleton named entities in noisy text.\n\nThe goal of this task is to provide a definition of emerging and of rare entities, and based on that, also datasets for detecting these entities.\n", "citation": "@inproceedings{derczynski-etal-2017-results,\n title = \"Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition\",\n author = \"Derczynski, Leon and\n Nichols, Eric and\n van Erp, Marieke and\n Limsopatham, Nut\",\n booktitle = \"Proceedings of the 3rd Workshop on Noisy User-generated Text\",\n month = sep,\n year = \"2017\",\n address = \"Copenhagen, Denmark\",\n publisher = \"Association for Computational Linguistics\",\n url = \"https://www.aclweb.org/anthology/W17-4418\",\n doi = \"10.18653/v1/W17-4418\",\n pages = \"140--147\",\n abstract = \"This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions.\n Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization),\n but recall on them is a real problem in noisy text - even among annotators.\n This drop tends to be due to novel entities and surface forms.\n Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'}\n hard to detect and resolve. The goal of this task is to provide a definition of emerging and of rare entities,\n and based on that, also datasets for detecting these entities. The task as described in this paper evaluated the\n ability of participating entries to detect and classify novel and emerging named entities in noisy text.\",\n}\n", "homepage": "http://noisy-text.github.io/2017/emerging-rare-entities.html", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "ner_tags": {"feature": {"num_classes": 13, "names": ["O", "B-corporation", "I-corporation", "B-creative-work", "I-creative-work", "B-group", "I-group", "B-location", "I-location", "B-person", "I-person", "B-product", "I-product"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "wnut_17", "config_name": "wnut_17", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1078379, "num_examples": 3394, "dataset_name": "wnut_17"}, "validation": {"name": "validation", "num_bytes": 259383, "num_examples": 1009, "dataset_name": "wnut_17"}, "test": {"name": "test", "num_bytes": 405536, "num_examples": 1287, "dataset_name": "wnut_17"}}, "download_checksums": {"https://raw.githubusercontent.com/leondz/emerging_entities_17/master/wnut17train.conll": {"num_bytes": 493781, "checksum": "731820e13f71af324c6b55a1575ec2ce59fbaa2a0806f8f0400b98d56cd6a7a5"}, "https://raw.githubusercontent.com/leondz/emerging_entities_17/master/emerging.dev.conll": {"num_bytes": 114749, "checksum": "e053b752b8155113ca66e7db08af5f2f5f43ab993694e4774f1ec5bad42312d8"}, "https://raw.githubusercontent.com/leondz/emerging_entities_17/master/emerging.test.annotated": {"num_bytes": 192425, "checksum": "2aa79b764e56ec9264a1b30fdd9b70195bd00ff400b62edd8f399d5f13c178f0"}}, "download_size": 800955, "post_processing_size": null, "dataset_size": 1743298, "size_in_bytes": 2544253}}