Open Access

Passage retrieval is an essential part of question answering systems. In this paper we use statistical language models to perform this task. Previous work has shown that language modeling techniques provide better results for both, document and passage retrieval. The motivation behind this paper is to define new smoothing methods for passage retrieval in question answering systems. The long term objective is to improve the quality of question answering systems to isolate the correct answer by choosing and evaluating the appropriate section of a document. In this work we use a three step approach. The first two steps are standard document and passage retrieval using the Lemur toolkit. As a novel contribution we propose as the third step a re-ranking using dedicated backing-off distributions. In particular backing-off from the passage-based language model to a language model trained on the document from which the passage is taken shows a significant improvement. For a TREC question answering task we can increase the mean average precision from 0.127 to 0.176.