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Connections between the lines: augmenting social networks with text

by: Jonathan Chang, Jordan Boyd-Graber, and David M. Blei

In: KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining New York, NY, USA: ACM (2009) , p. 169--178.

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URL:	http://portal.acm.org/citation.cfm?id=1557044
DOI:	10.1145/1557019.1557044

Network data is ubiquitous, encoding collections of relationships between entities such as people, places, genes, or corporations. While many resources for networks of interesting entities are emerging, most of these can only annotate connections in a limited fashion. Although relationships between entities are rich, it is impractical to manually devise complete characterizations of these relationships for every pair of entities on large, real-world corpora. In this paper we present a novel probabilistic topic model to analyze text corpora and infer descriptions of its entities and of relationships between those entities. We develop variational methods for performing approximate inference on our model and demonstrate that our model can be practically deployed on large corpora such as Wikipedia. We show qualitatively and quantitatively that our model can construct and annotate graphs of relationships and make useful predictions.

Description

Connections between the lines

BibTeX record

@inproceedings{Chang09textNetwork,
  abstract = {Network data is ubiquitous, encoding collections of relationships between entities such as people, places, genes, or corporations. While many resources for networks of interesting entities are emerging, most of these can only annotate connections in a limited fashion. Although relationships between entities are rich, it is impractical to manually devise complete characterizations of these relationships for every pair of entities on large, real-world corpora. In this paper we present a novel probabilistic topic model to analyze text corpora and infer descriptions of its entities and of relationships between those entities. We develop variational methods for performing approximate inference on our model and demonstrate that our model can be practically deployed on large corpora such as Wikipedia. We show qualitatively and quantitatively that our model can construct and annotate graphs of relationships and make useful predictions.},
  added-at = {2010-01-14T19:59:52.000+0100},
  address = {New York, NY, USA},
  author = {Chang, Jonathan and Boyd-Graber, Jordan and Blei, David M.},
  biburl = {http://www.bibsonomy.org/bibtex/25ddcf06e18cb1e3ba0232be987ecd953/lee_peck},
  booktitle = {KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining},
  description = {Connections between the lines},
  doi = {http://doi.acm.org/10.1145/1557019.1557044},
  interhash = {91c2d0e9a6625bc92333180b41b9c579},
  intrahash = {5ddcf06e18cb1e3ba0232be987ecd953},
  isbn = {978-1-60558-495-9},
  keywords = {09 Chang extraction network social text},
  location = {Paris, France},
  pages = {169--178},
  publisher = {ACM},
  timestamp = {2010-01-14T19:59:52.000+0100},
  title = {Connections between the lines: augmenting social networks with text},
  url = {http://portal.acm.org/citation.cfm?id=1557044},
  year = 2009
}

Endnote record

%0 Conference Paper
%1 Chang09textNetwork
%A Chang, Jonathan
%A Boyd-Graber, Jordan
%A Blei, David M.
%B KDD '09: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
%@ 978-1-60558-495-9
%C New York, NY, USA
%D 2009
%I ACM
%K 
%P 169--178
%T Connections between the lines: augmenting social networks with text
%R http://doi.acm.org/10.1145/1557019.1557044
%U http://portal.acm.org/citation.cfm?id=1557044
%X Network data is ubiquitous, encoding collections of relationships between entities such as people, places, genes, or corporations. While many resources for networks of interesting entities are emerging, most of these can only annotate connections in a limited fashion. Although relationships between entities are rich, it is impractical to manually devise complete characterizations of these relationships for every pair of entities on large, real-world corpora. In this paper we present a novel probabilistic topic model to analyze text corpora and infer descriptions of its entities and of relationships between those entities. We develop variational methods for performing approximate inference on our model and demonstrate that our model can be practically deployed on large corpora such as Wikipedia. We show qualitatively and quantitatively that our model can construct and annotate graphs of relationships and make useful predictions.