WordNet: a lexical database for English
Communications of the ACM
Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Using syntactic dependency as local context to resolve word sense ambiguity
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering word senses from a network of lexical cooccurrences
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks
Proceedings of the 17th international conference on World Wide Web
Identification of time-varying objects on the web
Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries
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As archives contain documents that span over a long period of time, the language used to create these documents and the language used for querying the archive can differ. This difference is due to evolution in both terminology and semantics and will cause a significant number of relevant documents being omitted. A static solution is to use query expansion based on explicit knowledge banks such as thesauri or ontologies. However as we are able to archive resources with more varied terminology, it will be infeasible to use only explicit knowledge for this purpose. There exist only few or no thesauri covering very domain specific terminologies or slang as used in blogs etc. In this Ph.D. thesis we focus on automatically detecting terminology evolution in a completely unsupervised manner as described in this technical paper.