copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

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

R. Balasubramanyan, B. Dalvi, and W. Cohen. ECML/PKDD (2), volume 8189 of Lecture Notes in Computer Science, page 628-642. Springer, (2013)

Links and resources

URL:

http://dblp.uni-trier.de/db/conf/pkdd/pkdd2013-2.html#BalasubramanyanDC13

BibTeX key:

conf/pkdd/BalasubramanyanDC13

search on:

Comments and Reviews
(0)

There is no review or comment yet. You can write one!

Cite this publication

@inproceedings{conf/pkdd/BalasubramanyanDC13,
  added-at = {2014-03-25T12:11:17.000+0100},
  author = {Balasubramanyan, Ramnath and Dalvi, Bhavana Bharat and Cohen, William W.},
  biburl = {https://puma.uni-kassel.de/bibtex/2e56623d21a1b7bcb442cd15fe098bb70/hotho},
  booktitle = {ECML/PKDD (2)},
  crossref = {conf/pkdd/2013-2},
  editor = {Blockeel, Hendrik and Kersting, Kristian and Nijssen, Siegfried and Zelezný, Filip},
  ee = {http://dx.doi.org/10.1007/978-3-642-40991-2_40},
  interhash = {9a32b7cc059a500ea302d0aa65036682},
  intrahash = {e56623d21a1b7bcb442cd15fe098bb70},
  isbn = {978-3-642-40990-5},
  keywords = {learning models sota supervised topic toread},
  pages = {628-642},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2014-03-25T12:11:17.000+0100},
  title = {From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering.},
  url = {http://dblp.uni-trier.de/db/conf/pkdd/pkdd2013-2.html#BalasubramanyanDC13},
  volume = 8189,
  year = 2013
}

%0 Conference Paper
%1 conf/pkdd/BalasubramanyanDC13
%A Balasubramanyan, Ramnath
%A Dalvi, Bhavana Bharat
%A Cohen, William W.
%B ECML/PKDD (2)
%D 2013
%E Blockeel, Hendrik
%E Kersting, Kristian
%E Nijssen, Siegfried
%E Zelezný, Filip
%I Springer
%K learning models sota supervised topic toread
%P 628-642
%T From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering.
%U http://dblp.uni-trier.de/db/conf/pkdd/pkdd2013-2.html#BalasubramanyanDC13
%V 8189
%@ 978-3-642-40990-5

PUMA

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

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

Links and resources

Comments and Reviews
(0)

Tags

Cite this publication

Meta data

community

tags (@hotho's tags highlighted)

PUMA

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML From Topic Models to Semi-supervised Learning: Biasing Mixed-Membership Models to Exploit Topic-Indicative Features in Entity Clustering.

Links and resources

Comments and Reviews (0)

Tags

Cite this publication

Meta data

community

tags (@hotho's tags highlighted)

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

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

Comments and Reviews
(0)