Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering
Hou, Jun (2014) Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering. PhD thesis, Queensland University of Technology.
This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops effective methods of entity-oriented retrieval, semantic relation identification and text clustering utilizing semantically annotated data. These methods are based on enriched text representation generated by introducing semantic information extracted from Wikipedia into the input text data. The proposed methods are evaluated against several start-of-art benchmarking methods on real-life data-sets. In particular, this thesis improves the performance of entity-oriented retrieval, identifies different lexical forms for an entity relation and handles clustering documents with multiple feature spaces.
Impact and interest:
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|Item Type:||QUT Thesis (PhD)|
|Supervisor:||Nayak, Richi & Zhang, Jinglan|
|Keywords:||Text Mining, Semantic Annotation, Entity-oriented Retrieval, Semantic Relation Identification, Clustering, Cluster Ensemble Learning, High-Order Co-Clustering, Multiple Subspace Learning, Concept-based Retrieval, Open Information Extraction|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Institution:||Queensland University of Technology|
|Deposited On:||17 Dec 2014 06:47|
|Last Modified:||08 Sep 2015 06:48|
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