Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering relations among named entities from large corpora
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Incorporating terminology evolution for query translation in text retrieval with association rules
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
MeSoOnTV: a media and social-driven ontology-based TV knowledge management system
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Mining semantics for culturomics: towards a knowledge-based approach
Proceedings of the 2013 international workshop on Mining unstructured big data using natural language processing
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This paper demonstrates a system called SITAC based on our proposed approach to automate the discovery of concepts (called SITACs) in text sources that are identical semantically but alter their names over time. This system is developed to perform time-aware translation of queries over text corpora by incorporating terminology evolution, thus providing more accurate responses to users, e.g., query processing on Mumbai should automatically take into account its former name Bombay. The SITAC system constitutes a novel collaborative framework of natural language processing, association rule mining and contextual similarity.