The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Proceedings of the 11th international conference on World Wide Web
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Using encyclopedic knowledge for automatic topic identification
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Ontologizing concept maps using graph theory
Proceedings of the 2011 ACM Symposium on Applied Computing
Towards open ontology learning and filtering
Information Systems
Simple supervised document geolocation with geodesic grids
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Conceptual Indexing of Documents Using Wikipedia
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Wikipedia-Based document categorization
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Mining interests for user profiling in electronic conversations
Expert Systems with Applications: An International Journal
A word-order based graph representation for relevance identification
Proceedings of the 21st ACM international conference on Information and knowledge management
Improved concept-based query expansion using Wikipedia
International Journal of Communication Networks and Distributed Systems
Cognitive canonicalization of natural language queries using semantic strata
ACM Transactions on Speech and Language Processing (TSLP)
A framework for automated construction of resource space based on background knowledge
Future Generation Computer Systems
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This paper presents a method for automatic topic identification using a graph-centrality algorithm applied to an encyclopedic graph derived from Wikipedia. When tested on a data set with manually assigned topics, the system is found to significantly improve over a simpler baseline that does not make use of the external encyclopedic knowledge.