Ad Hoc Table Retrieval using Intrinsic and Extrinsic Similarities

Abstract

Given a keyword query, the ad hoc table retrieval task aims at retrieving a ranked list of the top-k most relevant tables in a given table corpus. Previous works have primarily focused on designing table-centric lexical and semantic features, which could be utilized for learning-to-rank (LTR) tables. In this work, we make a novel use of intrinsic (passage-based) and extrinsic (manifold-based) table similarities for enhanced retrieval. Using the WikiTables benchmark, we study the merits of utilizing such similarities for this task. To this end, we combine both similarity types via a simple, yet an effective, cascade re-ranking approach. Overall, our proposed approach results in a significantly better table retrieval quality, which even transcends that of strong semantically-rich baselines.