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Fine-Grained Entity Linking

机译：细粒度实体联系

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摘要

The Entity Linking (EL) task involves linking mentions of entities in a text with their identifier in a Knowledge Base (KB) such as Wikipedia, BabelNet, DBpedia, Freebase, Wikidata, YAGO, etc. Numerous techniques have been proposed to address this task down through the years. However, not all works adopt the same convention regarding the entities that the EL task should target; for example, while some EL works target common entities like "interview"appearing in the KB, others only target named entities like "Michael Jackson". The lack of consensus on this issue (and others) complicates research on the EL task; for example, how can the performance of EL systems be evaluated and compared when systems may target different types of entities? In this work, we first design a questionnaire to understand what kinds of mentions and links the EL research community believes should be targeted by the task. Based on these results we propose a fine-grained categorization scheme for EL that distinguishes different types of mentions and links. We propose a vocabulary extension that allows to express such categories in EL benchmark datasets. We then relabel (subsets of) three popular EL datasets according to our novel categorization scheme, where we additionally discuss a tool used to semi-automate the labeling process. We next present the performance results of five EL systems for individual categories. We further extend EL systems with Word Sense Disambiguation and Coreference Resolution components, creating initial versions of what we call Fine-Grained Entity Linking (FEL) systems, measuring the impact on performance per category. Finally, we propose a configurable performance measure based on fuzzy sets that can be adapted for different application scenarios Our results highlight a lack of consensus on the goals of the EL task, show that the evaluated systems do indeed target different entities, and further reveal some open challenges for the (F)EL task regarding more complex forms of reference for entities. (C) 2020 Elsevier B.V. All rights reserved.
机译：该实体链接（EL）任务涉及在文本中与知识库（KB）中的标识符中的特征在文本中链接，例如维基百科，Babelnet，DBPedia，FreeBase，Wikidata，Yago等。已经提出了许多技术来解决此任务多年来。但是，并非所有作品都采用了对EL任务应目标的实体的同一公约;例如，虽然某些EL工作目标是在KB中出现“面试”这样的普通实体，但其他人只针对像“Michael Jackson”这样的名为实体。对这个问题（和其他人）缺乏共识，使研究成为EL任务的研究;例如，当系统针对不同类型的实体时，如何评估和比较EL系统的性能如何？在这项工作中，我们首先设计了一个调查问卷来了解EL研究社区认为应该由任务目标的精神和链接。基于这些结果，我们提出了一个细粒度的分类方案，用于区分不同类型的提到和链接。我们提出了一种词汇扩展，允许在EL基准数据集中表达此类类别。然后，我们根据我们的小型分类方案重新标记三个流行的EL数据集，我们还讨论了用于半自动标记过程的工具。我们接下来为个人类别提供五个EL系统的绩效结果。我们进一步扩展了EL Systems，具有单词义歧义和Coreference分辨率组件，创建了我们所谓的细粒度实体链接（FEL）系统的初始版本，从而测量每个类别的性能的影响。最后，我们提出了一种基于模糊集的可配置性能措施，可以适用于不同的应用场景，我们的结果突出了EL任务目标缺乏共识，表明评估系统确实是针对不同的实体，进一步揭示一些对（F）EL任务的开放挑战，了解更复杂的实体形式的参考。 （c）2020 Elsevier B.v.保留所有权利。

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