Cross-document Coreference Resolution over Predicted Mentions
Cross-document Coreference Resolution over Predicted Mentions
复制标题
针对预测提及的跨文档共指解析
DOI：
10.18653/v1/2021.findings-acl.453
复制
发表时间：
2021
期刊：
影响因子：
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通讯作者：
Ido Dagan
中科院分区：
文献类型：
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作者：
Arie Cattan;Alon Eirew;Gabriel Stanovsky;Mandar Joshi;Ido Dagan
共同参考分辨率主要在单个文档范围内进行研究，近年来基于端到端模型显示出令人印象深刻的进展。然而，跨文献（CD）共同参考分辨率的更具挑战性的任务仍然相对未被充分探索，最近的几个模型仅适用于黄金提及。在这里，我们介绍了第一个从原始文本进行CD共引用解析的端到端模型，它将文档内共引用解析的主要模型扩展到CD设置。我们的模型在金牌提及的事件和实体共同参考分辨率上取得了竞争结果。更重要的是，我们在标准ECB+数据集上设置了第一个基线结果，用于CD共同参考分辨率高于预测提及。此外，我们的模型比最近的CD共参考分辨率系统更简单、更有效，同时不使用任何外部资源。
Coreference resolution has been mostly investigated within a single document scope, showing impressive progress in recent years based on end-to-end models. However, the more challenging task of cross-document (CD) coreference resolution remained relatively under-explored, with the few recent models applied only to gold mentions. Here, we introduce the first end-to-end model for CD coreference resolution from raw text, which extends the prominent model for within-document coreference to the CD setting. Our model achieves competitive results for event and entity coreference resolution on gold mentions. More importantly, we set first baseline results, on the standard ECB+ dataset, for CD coreference resolution over predicted mentions. Further, our model is simpler and more efficient than recent CD coreference resolution systems, while not using any external resources.
DOI：
10.1145/3292500.3330929
发表时间：
2019-07
期刊：
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
影响因子：
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作者：
Nicholas Monath;Ari Kobren;A. Krishnamurthy;Michael R. Glass;A. McCallum
通讯作者：
Nicholas Monath;Ari Kobren;A. Krishnamurthy;Michael R. Glass;A. McCallum