Towards Better Text Understanding and Retrieval through Kernel Entity Salience Modeling

TL;DR

This paper introduces KESM, a kernel-based model that enhances text understanding and retrieval by accurately estimating entity importance in documents, leading to improved search performance.

Contribution

The paper proposes a novel end-to-end kernel entity salience model that leverages knowledge-enriched representations and kernel interactions for better entity importance estimation.

Findings

KESM outperforms frequency-based methods in entity salience detection.

KESM improves ad hoc search accuracy by modeling query entity salience.

Experimental results on multiple datasets validate KESM's effectiveness.

Abstract

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.