This paper presents a Kernel Entity Salience Model (KESM) that improves text
understanding and retrieval by better estimating entity salience (importance)
in documents. KESM represents entities by knowledge enriched distributed
representations, models the interactions between entities and words by kernels,
and combines the kernel scores to estimate entity salience. The whole model is
learned end-to-end using entity salience labels. The salience model also
improves ad hoc search accuracy, providing effective ranking features by
modeling the salience of query entities in candidate documents. Our experiments
on two entity salience corpora and two TREC ad hoc search datasets demonstrate
the effectiveness of KESM over frequency-based and feature-based methods. We
also provide examples showing how KESM conveys its text understanding ability
learned from entity salience to search.

本文提出一种核实体显著性模型（KESM），通过更好地估计文档中实体的显著性（重要性），提高文本理解和检索。KESM 通过知识增强的分布式表示来表示实体，通过核函数建模实体和单词之间的交互，并结合核分数来估计实体显著性。该模型的整个过程都是通过学习实体显著性标签来完成的。显著性模型还通过模拟候选文档中查询实体的显著性，提供有效的排名特征，从而提高了即席搜索的准确性。我们在两个实体显著性语料库和两个 TREC 即席搜索数据集上的实验结果表明，KESM 比基于频率和特征的方法更为有效。我们还提供了示例，展示了 KESM 如何将其从实体显著性中学到的文本理解能力应用于搜索。