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• W2398627862 abstract "Abstr act. Determining the semantic relatedness (SR) of two terms has been an appealing topic in information retrieval for many years as such information is useful for various tasks ranging from tag recommendation, over search query refinement to suggesting new web resources for the user to discover. Most approaches consider the SR of terms as static over time, and disregard the eventual temporal changes as imperfections. However, detecting and tracing changes in SR of terms over time may help in understanding the nature of changes in public opinion, as well as the change in the usage of terms in common language and jargon. In this paper, we propose an approach that makes use of microposts data in order to establish a dynamic measure of SR of terms, i.e., a measure that accounts for the changes in SR over time. We propose different scenarios of use (in online advertising and organizational knowledge management) which demonstrate the applicability of our approach in real life situations. We also provide a demo application for visualizing the change in micropost-based SR of terms." @default.
• W2398627862 created "2016-06-24" @default.
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• W2398627862 date "2011-01-01" @default.
• W2398627862 modified "2023-09-27" @default.
• W2398627862 title "Discovering the Dynamics of Terms' Semantic Relatedness through Twitter." @default.
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