Topic modeling on document networks with adjacent-encoder

• Main Authors: ZHANG, Ce, LAUW, Hady W.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Encoder architecture; Low-dimensional representation; Network structures; Semantic capture; Textual content; Topic Modeling; Artificial intelligence; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/5124; https://ink.library.smu.edu.sg/context/sis_research/article/6126/viewcontent/aaai20a.pdf
• Institution: Singapore Management University

Oftentimes documents are linked to one another in a network structure,e.g., academic papers cite other papers, Web pages link to other pages. In this paper we propose a holistic topic model to learn meaningful and unified low-dimensional representations for networked documents that seek to preserve...