Document Retrieval Using Entity-Based Language Models

Hadas Raviv, Oren Kurland, David Carmel

Abstract: We address the ad hoc document retrieval task by devising novel types of entity-based language models. The models utilize information about single terms in the query and documents as well as term sequences marked as entities by some entity-linking tool. The key principle of the language models is accounting, simultaneously, for the uncertainty inherent in the entity-markup process and the balance between using entity-based and term-based information. Empirical evaluation demonstrates the merits of using the language models for retrieval. For example, the performance transcends that of a state-of-the-art term proximity method. We also show that the language models can be effectively used for cluster-based document retrieval and query expansion.