This paper presents a word-entity duet framework for utilizing\u00a0knowledge bases in ad-hoc retrieval. In this work, the query and\u00a0documents are modeled by word-based representations and entitybased\u00a0representations. Ranking features are generated by the interactions\u00a0between the two representations, incorporating information\u00a0from the word space, the entity space, and the cross-space\u00a0connections through the knowledge graph. To handle the uncertainties\u00a0from the automatically constructed entity representations,\u00a0an attention-based ranking model AttR-Duet is developed. With\u00a0back-propagation from ranking labels, the model learns simultaneously\u00a0how to demote noisy entities and how to rank documents\u00a0with the word-entity duet. Evaluation results on TREC Web Track\u00a0ad-hoc task demonstrate that all of the four-way interactions in\u00a0the duet are useful, the attention mechanism successfully steers\u00a0the model away from noisy entities, and together they significantly\u00a0outperform both word-based and entity-based learning to rank\u00a0systems.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper presents a word-entity duet framework for utilizing\u00a0knowledge bases in ad-hoc retrieval. In this work, the query and\u00a0documents are modeled by word-based representations and entitybased\u00a0representations. Ranking features are generated by the interactions\u00a0between the two representations, incorporating information\u00a0from the word space, the entity space, and the cross-space\u00a0connections through the knowledge graph. 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