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Unpublished Paper
Group and Topic Discovery from Relations and Text
  • Xuerui Wang
  • Natasha Mohanty
  • Andrew McCallum, University of Massachusetts - Amherst
We present a probabilistic generative model of entity relationships and textual attributes that simultaneously discovers groups among the entities and topics among the corresponding text. Block-models of relationship data have been studied in social network analysis for some time. Here we simultaneously cluster in several modalities at once, incorporating the words associated with certain relationships. Significantly, joint inference allows the discovery of groups to be guided by the emerging topics, and vice-versa. We present experimental results on two large data sets: sixteen years of bills put before the U.S. Senate, comprising their corresponding text and voting records, and 43 years of similar data from the United Nations. We show that in comparison with traditional, separate latent-variable models for words or Blockstructures for votes, the Group-Topic model's joint inference improves both the groups and topics discovered.
  • Artificial Intelligence,
  • Database Management,
  • Database Applications,
  • data mining,
  • Graphical models,
  • text modeling,
  • relational learning
Publication Date
This is the pre-published version harvested from CIIR.
Citation Information
Xuerui Wang, Natasha Mohanty and Andrew McCallum. "Group and Topic Discovery from Relations and Text" (2005)
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