Text feature selection for relevance discovery: A fusion-based approach
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Abdullah Samaran A. Al Harbi Thesis
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Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. |
Description
This thesis presents innovative and effective feature selection models and frameworks to select and weight relevant features that describe user information needs. The proposed techniques fuse different text features to overcome problems in latent Dirichlet allocation and the relevant features discovered by existing relevance discovery algorithms. The proposed models and frameworks extend multiple random sets to model and understand the complex relationships between different entities that affect the weighting process of topical terms at both document- and collection-levels. The proposed techniques can reduce uncertainties in discovered relevant features and significantly improve the performance of text mining applications.
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| ID Code: | 199275 |
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| Item Type: | QUT Thesis (PhD) |
| Supervisor: | Li, Yuefeng & Xu, Yue |
| Additional Information: | Executive Dean’s Commendation for Outstanding Doctoral Thesis |
| Keywords: | Text Feature Selection, Relevance Discovery, User Information Needs, Random Sets, Uncertainty Reduction, Term Weighting, Feature Re-Ranking, Weight Scaling, Text Feature Fusion, Passage Relevance |
| DOI: | 10.5204/thesis.eprints.199275 |
| Divisions: | Past > QUT Faculties & Divisions > Science & Engineering Faculty Current > Schools > School of Mathematical Sciences |
| Institution: | Queensland University of Technology |
| Deposited On: | 23 Apr 2020 06:34 |
| Last Modified: | 01 Nov 2021 06:34 |
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