Passage retrieval: A probabilistic technique

Abstract

This paper presents a probabilistic technique to retrieve passages from texts having a large size or heterogeneous semantic content. The proposed technique is independent on any supporting auxiliary data, such as text structure, topic organization, or pre-defined text segments. A Bayesian framework implements the probabilistic technique. We carried out experiments to compare the probabilistic technique to one based on a text segmentation algorithm. In particular, the probabilistic technique is more effective than, or as effective as the one based on the text segmentation to retrieve small passages. Results show that passage size affects passage retrieval performance. Results do also suggest that text organization and query generality may have an impact on the difference in effectiveness between the two techniques.