ENT Rank: Retrieving Entities for Topical Information Needs through Entity-Neighbor-Text Relations

Abstract

Related work has demonstrated the helpfulness of utilizing information about entities in text retrieval; here we explore the converse: Utilizing information about text in entity retrieval. We model the relevance of Entity-Neighbor-Text (ENT) relations to derive a learning-to-rank-entities model. We focus on the task of retrieving (multiple) relevant entities in response to a topical information need such as "Zika fever". The ENT Rank model is designed to exploit semi-structured knowledge resources such as Wikipedia for entity retrieval. The ENT Rank model combines (1) established features of entity-relevance, with (2) information from neighboring entities (co-mentioned or mentioned-on-page) through (3) relevance scores of textual contexts through traditional retrieval models such as BM25 and RM3.