X. Wang, N. Mohanty, und A. Mccallum. KDD Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD), (2005)
Zusammenfassung
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.
KDD Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD)
Jahr
2005
comment
Extension to elaborate Author-Topic Models and the Stochastik Blockstructure Model.
Problem domain: voting in Politics
"The Group-Topic Model is a directed graphical model that
clusters entities with relations between them, as well as attributes
of those relations. The relations may be either directed
or undirected and have multiple attributes."
They have a rather complex graphical model, to which they apply Gibbs Sampling to get P(group of entity|topic) and P(topic of an event)
In fact, they model a single type of relation .oO(is this true?) with a binary attribute, which is the (dis-)agreement of the votes (yes/no/abundant).
Their future work: Application of the GT model to scientific citations and allowing mixtures of topics as a relation attribute.
%0 Conference Paper
%1 citeulike:393513
%A Wang, Xuerui
%A Mohanty, Natasha
%A Mccallum, Andrew
%B KDD Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD)
%D 2005
%K community sna topicinference
%T Group and Topic Discovery from Relations and Text
%U http://www.cs.umass.edu/~mccallum/papers/grouptopic_linkkdd05.pdf
%X 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.
@inproceedings{citeulike:393513,
abstract = {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.},
added-at = {2006-09-25T12:54:00.000+0200},
author = {Wang, Xuerui and Mohanty, Natasha and Mccallum, Andrew},
biburl = {https://www.bibsonomy.org/bibtex/25cb81f878ffc60ccda8ab45941bcf898/grahl},
booktitle = {KDD Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD)},
citeulike-article-id = {393513},
comment = {Extension to elaborate Author-Topic Models and the Stochastik Blockstructure Model.
Problem domain: voting in Politics
"The Group-Topic Model is a directed graphical model that
clusters entities with relations between them, as well as attributes
of those relations. The relations may be either directed
or undirected and have multiple attributes."
They have a rather complex graphical model, to which they apply Gibbs Sampling to get P(group of entity|topic) and P(topic of an event)
In fact, they model a single type of relation .oO(is this true?) with a binary attribute, which is the (dis-)agreement of the votes (yes/no/abundant).
Their future work: Application of the GT model to scientific citations and allowing mixtures of topics as a relation attribute.},
interhash = {fc58c22171e32fc00ec14e63d33a35dc},
intrahash = {5cb81f878ffc60ccda8ab45941bcf898},
keywords = {community sna topicinference},
priority = {0},
timestamp = {2007-07-25T11:36:55.000+0200},
title = {Group and Topic Discovery from Relations and Text},
url = {http://www.cs.umass.edu/~mccallum/papers/grouptopic_linkkdd05.pdf},
year = 2005
}