Hou, Yuexian; He, Liang; Zhao, Xiaozhao and Song, Dawei
PDF (Accepted Manuscript)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (242Kb) | Preview
|DOI (Digital Object Identifier) Link:||https://doi.org/10.1007/978-3-642-23318-0_8|
|Google Scholar:||Look up in Google Scholar|
The classical bag-of-word models fail to capture contextual associations between words. We propose to investigate the “high-order pure dependence” among a number of words forming a semantic entity, i.e., the high-order dependence that cannot be reduced to the random coincidence of lower-order dependence. We believe that identifying these high-order pure dependence patterns will lead to a better representation of documents. We first present two formal definitions of pure dependence: Unconditional Pure Dependence (UPD) and Conditional Pure Depen- dence (CPD). The decision on UPD or CPD, however, is a NP-hard problem. We hence prove a series of sufficient criteria that entail UPD and CPD, within the well-principled Information Geometry (IG) framework, leading to a more feasible UPD/CPD identification procedure. We further develop novel methods to extract word patterns with high-order pure dependence, which can then be used to extend the original unigram document models. Our methods are evaluated in the context of query ex- pansion. Compared with the original unigram model and its extensions with term associations derived from constant n-grams and Apriori association rule mining, our IG-based methods have proved mathematically more rigorous and empirically more effective.
|Item Type:||Conference Item|
|Copyright Holders:||2011 Springer|
|Extra Information:||Received best paper award.
Published in: Proceedings ICTIR'11 Proceedings of the Third international conference on Advances in information retrieval theory, pages 64-76, Springer, ISBN: 978-3-642-23317-3.
|Academic Unit/School:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Dawei Song|
|Date Deposited:||16 Oct 2012 11:02|
|Last Modified:||01 Dec 2016 00:51|
|Share this page:|
Download history for this item
These details should be considered as only a guide to the number of downloads performed manually. Algorithmic methods have been applied in an attempt to remove automated downloads from the displayed statistics but no guarantee can be made as to the accuracy of the figures.