Latent Dirichlet Allocation for Automatic Document Categorization
I. Bíró, und J. Szabó.
Machine Learning and Knowledge Discovery in Databases (2009)

In this paper we introduce and evaluate a technique for applying latent Dirichlet allocation to supervised semantic categorization of documents. In our setup, for every category an own collection of topics is assigned, and for a labeled training documentonly topics from its category are sampled. Thus, compared to the classical LDA that processes the entire corpus in one, weessentially build separate LDA models for each category with the category-specific topics, and then these topic collectionsare put together to form a unified LDA model. For an unseen document the inferred topic distribution gives an estimation howmuch the document fits into the category.

URL

http://dx.doi.org/10.1007/978-3-642-04174-7_28

Suchen auf

Diese Publikation wurde noch nicht bewertet.

Bewertungsverteilung

Durchschnittliche Benutzerbewertung0,0 von 5.0 auf Grundlage von 0 Rezensionen

Bitte melden Sie sich an um selbst Rezensionen oder Kommentare zu erstellen.

@article{istván2009latent,
  abstract = {In this paper we introduce and evaluate a technique for applying latent Dirichlet allocation to supervised semantic categorization
of documents. In our setup, for every category an own collection of topics is assigned, and for a labeled training documentonly topics from its category are sampled. Thus, compared to the classical LDA that processes the entire corpus in one, weessentially build separate LDA models for each category with the category-specific topics, and then these topic collectionsare put together to form a unified LDA model. For an unseen document the inferred topic distribution gives an estimation howmuch the document fits into the category.},
  added-at = {2010-05-04T08:55:46.000+0200},
  author = {Bíró, István and Szabó, Jácint},
  biburl = {https://puma.uni-kassel.de/bibtex/22db7477d992284eabea47e1c9669ab5a/folke},
  interhash = {f4c5b12409be4108320cba5b8fd18c45},
  intrahash = {2db7477d992284eabea47e1c9669ab5a},
  journal = {Machine Learning and Knowledge Discovery in Databases},
  keywords = {2009 ecml lda mining pkdd text},
  pages = {430--441},
  timestamp = {2010-05-04T08:55:48.000+0200},
  title = {Latent Dirichlet Allocation for Automatic Document Categorization},
  url = {http://dx.doi.org/10.1007/978-3-642-04174-7_28},
  year = 2009
}

%0 Journal Article
%1 istván2009latent
%A Bíró, István
%A Szabó, Jácint
%D 2009
%J Machine Learning and Knowledge Discovery in Databases
%K 2009 ecml lda mining pkdd text
%P 430--441
%T Latent Dirichlet Allocation for Automatic Document Categorization
%U http://dx.doi.org/10.1007/978-3-642-04174-7_28
%X In this paper we introduce and evaluate a technique for applying latent Dirichlet allocation to supervised semantic categorization
of documents. In our setup, for every category an own collection of topics is assigned, and for a labeled training documentonly topics from its category are sampled. Thus, compared to the classical LDA that processes the entire corpus in one, weessentially build separate LDA models for each category with the category-specific topics, and then these topic collectionsare put together to form a unified LDA model. For an unseen document the inferred topic distribution gives an estimation howmuch the document fits into the category.

PUMA

Latent Dirichlet Allocation for Automatic Document Categorization
I. Bíró, und J. Szabó.
Machine Learning and Knowledge Discovery in Databases (2009)

Tags

Nutzer

Kommentare und Rezensionen

Zitieren Sie diese Publikation

PUMA

Latent Dirichlet Allocation for Automatic Document CategorizationI. Bíró, und J. Szabó. Machine Learning and Knowledge Discovery in Databases (2009)

Tags

Nutzer

Kommentare und Rezensionen

Zitieren Sie diese Publikation

Latent Dirichlet Allocation for Automatic Document Categorization
I. Bíró, und J. Szabó.
Machine Learning and Knowledge Discovery in Databases (2009)