From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering. - Citegraph

Paper Info

Title
From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering.

Year	DOI	Venue
2013	10.1007/978-3-642-40991-2_40	ECML/PKDD
Field	DocType	Citations
Data mining,Latent Dirichlet allocation,Semi-supervised learning,Computer science,Computational linguistics,Approximate inference,Latent variable,Artificial intelligence,Topic model,Cluster analysis,Machine learning,Gibbs sampling	Conference	5
PageRank	References	Authors
0.42	22	3

Authors (3 rows)

Cited by (5 rows)

References (22 rows)

Name	Order	Citations	PageRank
Ramnath Balasubramanyan	1	688	40.20
Bhavana Bharat Dalvi	2	201	17.31
William W. Cohen	3	10178	1243.74

1