13 citations · 38 across the 9 of their papers we have counts for
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cs.IR2023
ATEM: A Topic Evolution Model for the Detection of Emerging Topics in Scientific Archives
Hamed Rahimi, Hubert Naacke, Camelia Constantin +1
This paper presents ATEM, a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that…
cs.IR2023★ 4 cited
ANTM: An Aligned Neural Topic Model for Exploring Evolving Topics
Hamed Rahimi, Hubert Naacke, Camelia Constantin +1
This paper presents an algorithmic family of dynamic topic models called Aligned Neural Topic Models (ANTM), which combine novel data mining algorithms to provide a modular framewo…