biu-nlp/CoreSearch
收藏资源简介:
CoreSearch数据集是一个大规模的数据集,用于跨文档事件共指搜索任务。该数据集来源于Wikipedia,并利用了Wikipedia Event Coreference数据集(WEC-Eng)的注释。数据集包含训练、验证和测试集,总共有2,772,968个段落,其中包含3,110个带有提及的段落和522个事件簇。数据集的语言为英语,提供了加载数据集的代码示例。
数据集名称
CoreSearch Dataset
数据集描述
CoreSearch Dataset是一个大规模的跨文档事件共指搜索数据集。
数据集版本
- CoreSearchV2: 此数据集的一个更清洁版本现已可在https://huggingface.co/datasets/biu-nlp/CoreSearchV2获取。
语言
英语
数据分割
- 最终版本的CD事件共指搜索数据集
| Train | Valid | Test | Total | |
|---|---|---|---|---|
| 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
此数据集根据<a href="https://creativecommons.org/licenses/by-sa/3.0/deed.en_US">Creative Commons Attribution-ShareAlike 3.0 Unported License</a>提供。



