Topic modeling【经典模型】

时间：2014-07-29 12:04:26 阅读：455 评论：0 收藏：0 [点我收藏+]

http://www.cs.princeton.edu/~blei/topicmodeling.html

Topic models are a suite of algorithms that uncover the hidden thematic structure in document collections. These algorithms help us develop new ways to search, browse and summarize large archives of texts.

Below, you will find links to introductory materials, corpus browsers based on topic models, and open source software (from my research group) for topic modeling.

Introductory materials

I wrote a general introduction to topic modeling .
John Lafferty and I wrote a more technical review paper about this field.
Here are slides from some recent tutorials about topic modeling:
Here is a video from a talk on dynamic and correlated topic models applied to the journal Science . (Here are the slides.)
David Mimno maintains a bibliography of topic modeling papers and software.
The topic models mailing list is a good forum for discussing topic modeling.

Corpus browsers based on topic models

The structure uncovered by topic models can be used to explore an otherwise unorganized collection. The following are browsers of large collections of documents, built with topic models.

A 100-topic browser of the dynamic topic model fit to Science (1882-2001).
A 100-topic browser of the correlated topic model fit to Science (1980-2000)
A 50-topic browser of latent Dirichlet allocation fit to the 2006 arXiv.
A 20-topic browser of latent Dirichlet allocation fit to The American Political Science Review

Also see Sean Gerrish‘s discipline browser for an interesting application of topic modeling at JSTOR.

To build your own browsers, see Allison Chaney‘s excellent Topic Model Visualization Engine (TMVE). For example, here is a browser of 100,000 Wikipedia articles that uses TMVE.

Topic modeling software

Our research group has released many open-source software packages for topic modeling. Please post questions, comments, and suggestions about this code to the topic models mailing list.

Link	Model/Algorithm	Language	Author	Notes
lda-c	Latent Dirichlet allocation	C	D. Blei	This implements variational inference for LDA.
class-slda	Supervised topic models for classifiation	C++	C. Wang	Implements supervised topic models with a categorical response.
lda	R package for Gibbs sampling in many models	R	J. Chang	Implements many models and is fast . Supports LDA, RTMs (for networked documents), MMSB (for network data), and sLDA (with a continuous response).
online lda	Online inference for LDA	Python	M. Hoffman	Fits topic models to massive data. The demo downloads random Wikipedia articles and fits a topic model to them.
online hdp	Online inference for the HDP	Python	C. Wang	Fits hierarchical Dirichlet process topic models to massive data. The algorithm determines the number of topics.
tmve (online)	Topic Model Visualization Engine	Python	A. Chaney	A package for creating corpus browsers. See, for example,Wikipedia .
ctr	Collaborative modeling for recommendation	C++	C. Wang	Implements variational inference for a collaborative topic models. These models recommend items to users based on item content and other users‘ ratings.
dtm	Dynamic topic models and the influence model	C++	S. Gerrish	This implements topics that change over time and a model of how individual documents predict that change.
hdp	Hierarchical Dirichlet processes	C++	C. Wang	Topic models where the data determine the number of topics. This implements Gibbs sampling.
ctm-c	Correlated topic models	C	D. Blei	This implements variational inference for the CTM.
diln	Discrete infinite logistic normal	C	J. Paisley	This implements the discrete infinite logistic normal, a Bayesian nonparametric topic model that finds correlated topics.
hlda	Hierarchical latent Dirichlet allocation	C	D. Blei	This implements a topic model that finds a hierarchy of topics. The structure of the hierarchy is determined by the data.
turbotopics	Turbo topics	Python	D. Blei	Turbo topics find significant multiword phrases in topics.