Utilizing Passage-Based Language Models for Document Retrieval

Abstract. We show that several previously proposed passage-based document ranking principles, along with some new ones, can be derived from the same probabilistic model. We use language models to instantiate specific algorithms, and propose a passage language model that integrates information from the ambient document to an extent controlled by the estimated document homogeneity. Several document-homogeneity measures that we propose yield passage language models that are more effective than the standard passage model for basic document retrieval and for constructing and utilizing passage-based relevance models; the latter outperform a document-based relevance model. We also show that the homogeneity measures are effective means for integrating documentquery and passage-query similarity information for document retrieval.

• Type: Conference
• Year: 2008
• Where: ECIR
• Authors: Michael Bendersky, Oren Kurland
• Keywords: Document, ECIR 2008, Information Technology, Language Models, Passage Language Models