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
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Finding related pages using Green measures: an illustration with Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Topic identification using Wikipedia graph centrality
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
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
Mining interests for user profiling in electronic conversations
Expert Systems with Applications: An International Journal
Proceedings of the Workshop on Semantic Analysis in Social Media
Collaboratively built semi-structured content and Artificial Intelligence: The story so far
Artificial Intelligence
Computing text semantic relatedness using the contents and links of a hypertext encyclopedia
Artificial Intelligence
Unsupervised graph-based topic labelling using dbpedia
Proceedings of the sixth ACM international conference on Web search and data mining
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 an encyclopedic graph derived from Wikipedia. The system is found to exceed the performance of previously proposed machine learning algorithms for topic identification, with an annotation consistency comparable to human annotations.