为实体集生成类别
Generating Categories for Sets of Entities
摘要：
范畴系统是知识库的核心组成部分，因为它们提供了语义相关概念和实体的层次分组。它们是一种独特而宝贵的资源，可用于广泛的信息访问任务。为了帮助知识编辑者手动扩展类别系统，本文提出了一种为实体集生成类别的方法。首先，我们采用神经抽象摘要模型来生成候选类别。接下来，为每个候选者标识层次结构内的位置。最后，使用基于结构、内容和层次的特征对候选人进行排名，以确定最有希望的候选人（根据特异性、层次和重要性进行测量）。我们开发了一个基于维基百科类别的测试集合，并证明了所提出的方法的有效性。
英文摘要：
Category systems are central components of knowledge bases, as they provide a hierarchical grouping of semantically related concepts and entities. They are a unique and valuable resource that is utilized in a broad range of information access tasks. To aid knowledge editors in the manual process of expanding a category system, this paper presents a method of generating categories for sets of entities. First, we employ neural abstractive summarization models to generate candidate categories. Next, the location within the hierarchy is identified for each candidate. Finally, structure-, content-, and hierarchy-based features are used to rank candidates to identify by the most promising ones (measured in terms of specificity, hierarchy, and importance). We develop a test collection based on Wikipedia categories and demonstrate the effectiveness of the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
