The Pseudo-relevance Feedback Model Based on Quantum Probability Theory

DOI

10.2991/sekeie-14.2014.43How to use a DOI?

Keywords

Information retrieval; pseudo relevance feedback; quantum probability theory; document weight allocation

Abstract

Relevance Model (RM) is one of typical and generally stable methods for the query expansion in information retrieval (IR). This paper presents a novel information retrieval model based on the quantum probability theory, and makes a preliminary exploration on the application of quantum model in pseudo-relevance feedback. In the document weight allocation framework, we propose two re-ranking approaches based on linear weight allocation and quantum interference weight allocation, respectively. The experimental results on standard TREC datasets show that the proposed model is effective and potential in information retrieval tasks.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yueheng Sun
AU  - Chenjun Zou
PY  - 2014/03
DA  - 2014/03
TI  - The Pseudo-relevance Feedback Model Based on Quantum Probability Theory
BT  - Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014)
PB  - Atlantis Press
SP  - 184
EP  - 187
SN  - 1951-6851
UR  - https://doi.org/10.2991/sekeie-14.2014.43
DO  - 10.2991/sekeie-14.2014.43
ID  - Sun2014/03
ER  -