Hou, Yuexian; Zhao, Ziaozhao; Song, Dawei and Li, Weijie
(2013).
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(Version of Record)
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| DOI (Digital Object Identifier) Link: | https://doi.org/10.1145/2493175.2493177 |
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| Google Scholar: | Look up in Google Scholar |
Abstract
The classical bag-of-word models for Information Retrieval (IR) fail to capture contextual associations between words. In this paper, we propose to investigate the “pure high-order dependence” among a number of words forming an un-separable semantic entity, i.e., the high-order dependence that cannot be reduced to the random coincidence of lower-order dependencies. We believe that identifying these pure high-order dependence patterns will lead to a better representation of documents and novel retrieval models. Specifically, two formal definitions of pure dependence: Unconditional Pure Dependence (UPD) and Conditional Pure Dependence (CPD) are presented. The decision on UPD and CPD, however, is a NP-hard in general.
We hence prove the 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 pure high-order dependence. Our methods are applied to and extensively evaluated on three typical IR tasks: text classification, and text retrieval without and with query expansion.
| Item Type: | Journal Item |
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| Copyright Holders: | 2013 ACM |
| ISSN: | 1558-2868 |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Related URLs: |
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| Item ID: | 36460 |
| Depositing User: | Dawei Song |
| Date Deposited: | 31 Jan 2013 11:01 |
| Last Modified: | 02 May 2018 13:49 |
| URI: | http://oro.open.ac.uk/id/eprint/36460 |
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