A hybrid information retrieval model using metadata and text

Abstract

Information retrieval (IR) with metadata tends to have high precision as long as the user expresses the information need accurately but may suffer from low recall because queries are too exact with the specification of the metadata fields. On the other hand, full-text retrieval tends to suffer more from low precision especially when queries are simple and the number of documents is large. While structured queries targeted at metadata can be quite precise and the retrieval results can be accurate, it is not easy to construct an effective structured query without understanding the characteristics of the metadata. Casual users, however, are usually interested in spending time to understand the meaning of various metadata. In this paper, we propose a hybrid IR model that searches both metadata and text fields of documents. User queries are analyzed and converted into a hybrid query automatically. Experiments show that the hybrid approach outperforms either of the cases, i.e. searching text only or metadata only.