@inproceedings{cd33a19aa9d24114b5fc81d3605563e7,
title = "Facilitating query decomposition in query language modeling by association rule mining using multiple sliding windows",
abstract = "This paper presents a novel framework to further advance the recent trend of using query decomposition and high-order term relationships in query language modeling, which takes into account terms implicitly associated with different subsets of query terms. Existing approaches, most remarkably the language model based on the Information Flow method are however unable to capture multiple levels of associations and also suffer from a high computational overhead. In this paper, we propose to compute association rules from pseudo feedback documents that are segmented into variable length chunks via multiple sliding windows of different sizes. Extensive experiments have been conducted on various TREC collections and our approach significantly outperforms a baseline Query Likelihood language model, the Relevance Model and the Information Flow model.",
keywords = "Association rule, Document segmentation, Query expansion, Term relationship",
author = "Dawei Song and Qiang Huang and Stefan R{\"u}ger and Peter Bruza",
year = "2008",
doi = "10.1007/978-3-540-78646-7\_31",
language = "English",
isbn = "3540786457",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "334--345",
booktitle = "Advances in Information Retrieval - 30th European Conference on IR Research, ECIR 2008, Proceedings",
note = "30th Annual European Conference on Information Retrieval, ECIR 2008 ; Conference date: 30-03-2008 Through 03-04-2008",
}