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Dedicated Backing-Off Distributions for Language Model Based Passage

  • 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.

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Author:Munawar Hussain, Andreas Merkel, Dietrich Klakow
Parent Title (German):LWA 2006 : Lernen – Wissensentdeckung – Adaptivität (9.–11.10.2006 in Hildesheim)
Document Type:Conference Proceeding
Date of Publication (online):2011/04/21
Year of first Publication:2006
Contributing Corporation:Spoken Language Systems Saarland University, Saarbr¨ucken
Release Date:2011/04/21
First Page:138
Last Page:143
PPN:Link zum Katalog
Institutes:Fachbereich IV / Informatik
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Licence (German):License LogoUrheberrechtlich geschützt