This paper explores Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to deal with unseen entities without annotation. A compromised solution is to build a global entity-unspecific model for all entities without respect to the relationship information among entities, which cannot guarantee achieving a satisfactory result for each entity. Moreover, most previous methods can not adequately exploit prior knowledge embedded in entities or documents due to considering all kinds of features indifferently. In this paper, we propose a novel entity and document class-dependent discriminative mixture model by introducing one intermediate layer to model the correlation between entity-document pairs and hybrid latent entity-document classes. The model can better adjust to different types of entities and documents, and achieve better performance when dealing with a broad range of entity and document classes. An extensive set of experiments has been conducted on two offical datasets, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.

中文翻译：

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用于累积引文推荐的混合判别混合模型

本文探讨了知识库加速 (KBA) 的累积引文推荐 (CCR)。CCR 任务旨在从大量按时间排序的流语料库中检测一组具有优先级的目标实体的潜在引用。以前为每个实体构建单独相关性模型的 CCR 方法无法在没有注释的情况下处理看不见的实体。一个折衷的解决方案是为所有实体建立一个全局的实体非特定模型，而不考虑实体之间的关系信息，这不能保证每个实体都能获得满意的结果。此外，由于对各种特征的无差别考虑，大多数以前的方法无法充分利用嵌入在实体或文档中的先验知识。在本文中，我们通过引入一个中间层来模拟实体-文档对和混合潜在实体-文档类之间的相关性，提出了一种新的实体和文档类相关判别混合模型。该模型可以更好地适应不同类型的实体和文档，并在处理广泛的实体和文档类时获得更好的性能。已经在两个官方数据集上进行了大量实验，实验结果表明所提出的模型可以达到最先进的性能。并在处理广泛的实体和文档类时获得更好的性能。已经在两个官方数据集上进行了大量实验，实验结果表明所提出的模型可以达到最先进的性能。并在处理广泛的实体和文档类时获得更好的性能。已经在两个官方数据集上进行了大量实验，实验结果表明所提出的模型可以达到最先进的性能。