  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.
