AAAI
Association for the Advancement of Artificial Intelligence
Amal Kaluarachchi
Montclair State University
Aparna Warde
Montclair State University
Jing Peng
Montclair State University
Anna Feldman
Montclair State University
10.1609/aaai.v24i1.7778
This paper describes a system called SITAC based on our proposed approach to discover concepts (called SITACs) in text archives that are identical semantically but alter their names over time. Our approach integrates natural language processing, association rule mining and contextual similarity to discover SITACs in order to answer historical queries over text corpora.
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