"wnut_17" 数据集卡片

数据集摘要

WNUT 17：新兴和罕见实体识别

这个共享任务旨在识别新兴讨论中的不寻常、以前未见过的实体。命名实体构成许多现代方法的基础（如事件聚类和摘要），但是在嘈杂的文本中回溯它们是一个真正的问题，即使在标注者之间也存在这个问题。这种下降往往是由于新的实体和表面形式。例如，考虑一下推文 "so.. kktny in 30 mins?" - 即使是人类专家也很难检测和解析实体kktny。该任务将评估在嘈杂的文本中检测和分类新兴、单个命名实体的能力。

该任务的目标是提供新兴和罕见实体的定义，并基于此定义提供用于检测这些实体的数据集。

支持的任务和排行榜

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语言

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数据集结构

数据实例

• 下载的数据集文件大小：0.80 MB
• 生成的数据集大小：1.74 MB
• 总磁盘使用量：2.55 MB

'train' 的一个示例如下所示。

{
    "id": "0",
    "ner_tags": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 7, 8, 8, 0, 7, 0, 0, 0, 0, 0, 0, 0, 0],
    "tokens": ["@paulwalk", "It", "'s", "the", "view", "from", "where", "I", "'m", "living", "for", "two", "weeks", ".", "Empire", "State", "Building", "=", "ESB", ".", "Pretty", "bad", "storm", "here", "last", "evening", "."]
}

数据字段

所有拆分之间的数据字段是相同的：

• id（字符串）：示例的ID。
• tokens（字符串列表）：示例文本的标记。
• ner_tags（类别标签列表）：标记的NER标签（使用IOB2格式），可能的值有：
  • 0：O
  • 1：B-corporation
  • 2：I-corporation
  • 3：B-creative-work
  • 4：I-creative-work
  • 5：B-group
  • 6：I-group
  • 7：B-location
  • 8：I-location
  • 9：B-person
  • 10：I-person
  • 11：B-product
  • 12：I-product

数据拆分

数据集创建

策划理由

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源数据

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注释

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个人和敏感信息

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使用数据的注意事项

数据集的社会影响

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偏见讨论

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其他已知限制

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附加信息

数据集策划者

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许可信息

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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://www.aclweb.org/anthology/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.",
}

贡献

感谢 @thomwolf ， @lhoestq ， @stefan-it ， @lewtun ， @jplu 添加此数据集。