The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Creating a Web community chart for navigating related communities
Proceedings of the 12th ACM conference on Hypertext and Hypermedia
Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
Stochastic link and group detection
Eighteenth national conference on Artificial intelligence
PRINCIPAR: an efficient, broad-coverage, principle-based parser
COLING '94 Proceedings of the 15th conference on Computational linguistics - Volume 1
Discovering corpus-specific word senses
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
CDPM: Finding and Evaluating Community Structure in Social Networks
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
JCCM: Joint Cluster Communities on Attribute and Relationship Data in Social Networks
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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Although community discovery based on social network analysis has been studied extensively in the Web hyperlink environment, limited research has been done in the case of named entities in text documents. The co-occurrence of entities in documents usually implies some connections among them. Investigating such connections can reveal important patterns. In this paper, we mine communities among named entities in Web documents and text corpus. Most existing works on community discovery generate a partition of the entity network, assuming each entity belongs to one community. However, in the scenario of named entities, an entity may participate in several communities. For example, a person is in the communities of his/her family, colleagues, and friends. In this paper, we propose a novel technique to mine overlapping communities of named entities. This technique is based on triangle formation, expansion, and clustering with content similarity. Our experimental results show that the proposed technique is highly effective.