National Research Foundation (NRF) Singapore under NRF Fellowship Programm

LAUW, Hady W.

ZHANG, Ce

English

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 both textual content and network structure. On the basis of reconstructing not only the input document but also its adjacent neighbors, we develop two neural encoder architectures. Adjacent-Encoder, or AdjEnc, induces competition among documents for topic propagation, and reconstruction among neighbors for semantic capture. Adjacent-Encoder-X, or AdjEnc-X, extends this to also encode the network structure in addition to document content. We evaluate our models on real-world document networks quantitatively and qualitatively, outperforming comparable baselines comprehensively

Zhang, Ce

Lauw, Hady W.

Association for the Advancement of Artificial Intelligence: AAAI Publications

Topic Modeling on Document Networks with Adjacent-Encoder

Institutional Knowledge at Singapore Management University

Topic modeling on document networks with adjacent-encoder

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6126&amp;context=sis_research

Topic modeling on document networks with adjacent-encoder

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Association for the Advancement of Artificial Intelligence: AAAI Publications

Institutional Knowledge at Singapore Management University