Intel/CoreSearch

CoreSearch数据集是一个大规模的数据集，专门用于跨文档事件共指搜索。该数据集支持英语，并提供了一个更干净的版本CoreSearchV2。数据集包含训练集、验证集和测试集，详细记录了每个部分中的簇数、段落数和总段落数。该数据集基于Wikipedia内容，并遵循Creative Commons Attribution-ShareAlike 3.0 Unported License。

原始信息汇总

数据集概述

名称： The CoreSearch Dataset
版本： CoreSearchV2
用途： 用于跨文档事件共指搜索
语言： 英语

数据集版本

• CoreSearchV2: 该数据集的更清洁版本可在https://huggingface.co/datasets/biu-nlp/CoreSearchV2获取。

数据加载

• 数据集文件可通过遵循Huggingface Hub的指示进行读取/下载。
• 示例代码展示了如何加载CoreSearch DPR文件夹。

数据分割

• 最终版本的跨文档事件共指搜索数据集

  	训练	验证	测试	总计
  WEC-Eng验证数据
  # 集群	237	49	236	522
  # 包含提及的段落	1,503	341	1,266	3,110
  # 添加的破坏者段落	922,736	923,376	923,746	2,769,858
  # 总段落	924,239	923,717	925,012	2,772,968

引用信息

@inproceedings{eirew-etal-2022-cross, title = "Cross-document Event Coreference Search: Task, Dataset and Modeling", author = "Eirew, Alon and Caciularu, Avi and Dagan, Ido", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, United Arab Emirates", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.emnlp-main.58", pages = "900--913", abstract = "The task of Cross-document Coreference Resolution has been traditionally formulated as requiring to identify all coreference links across a given set of documents. We propose an appealing, and often more applicable, complementary set up for the task {--} Cross-document Coreference Search, focusing in this paper on event coreference. Concretely, given a mention in context of an event of interest, considered as a query, the task is to find all coreferring mentions for the query event in a large document collection. To support research on this task, we create a corresponding dataset, which is derived from Wikipedia while leveraging annotations in the available Wikipedia Event Coreferecene dataset (WEC-Eng). Observing that the coreference search setup is largely analogous to the setting of Open Domain Question Answering, we adapt the prominent Deep Passage Retrieval (DPR) model to our setting, as an appealing baseline. Finally, we present a novel model that integrates a powerful coreference scoring scheme into the DPR architecture, yielding improved performance.", }

许可证

• 数据集根据<a href="https://creativecommons.org/licenses/by-sa/3.0/deed.en_US">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>提供。