"tner/wnut2017" 数据集卡片

数据集摘要

WNUT 2017 NER 数据集格式化为 TNER 项目的一部分。

• 实体类型：creative-work，corporation，group，location，person，product

数据集结构

数据实例

train 的一个示例如下所示。

{
    'tokens': ['@paulwalk', 'It', "'s", 'the', 'view', 'from', 'where', 'I', "'m", 'living', 'for', 'two', 'weeks', '.', 'Empire', 'State', 'Building', '=', 'ESB', '.', 'Pretty', 'bad', 'storm', 'here', 'last', 'evening', '.'],
    'tags': [12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 3, 9, 9, 12, 3, 12, 12, 12, 12, 12, 12, 12, 12]
}

标签ID

label2id 字典可以在 here 找到。

{
    "B-corporation": 0,
    "B-creative-work": 1,
    "B-group": 2,
    "B-location": 3,
    "B-person": 4,
    "B-product": 5,
    "I-corporation": 6,
    "I-creative-work": 7,
    "I-group": 8,
    "I-location": 9,
    "I-person": 10,
    "I-product": 11,
    "O": 12
}

数据拆分

引用信息

@inproceedings{derczynski-etal-2017-results,
    title = "Results of the {WNUT}2017 Shared Task on Novel and Emerging Entity Recognition",
    author = "Derczynski, Leon  and
      Nichols, Eric  and
      van Erp, Marieke  and
      Limsopatham, Nut",
    booktitle = "Proceedings of the 3rd Workshop on Noisy User-generated Text",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W17-4418",
    doi = "10.18653/v1/W17-4418",
    pages = "140--147",
    abstract = "This shared task focuses on identifying unusual, previously-unseen entities in the context of emerging discussions. Named entities form the basis of many modern approaches to other tasks (like event clustering and summarization), but 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. Take for example the tweet {``}so.. kktny in 30 mins?!{''} {--} even human experts find the entity {`}kktny{'} hard to detect and resolve. The 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. The task as described in this paper evaluated the ability of participating entries to detect and classify novel and emerging named entities in noisy text.",
}