Proximity 2 -aware ranking for textual, temporal, and geographic queries

Abstract

Temporal and geographic information needs are frequent and important but not well served by standard IR systems. Recent approaches address such needs by extracting and normalizing temporal and geographic expressions from documents. They calculate specific scores for the temporal and/ or geographic parts of a query. However, all approaches assume independence between the different query parts. In this paper, we present a new model to rank documents according to combined textual, temporal, and geographic queries. The independence assumption between the query parts is eliminated by calculating proximity scores. Thus, documents are regarded to be more relevant if terms and expressions satisfying the different query parts occur close to each other in a document. As our evaluations based on the NTCIR-GeoTime data show, our proposed model outperforms baseline models that do not use proximity information.