Fit a NUBBI model, which takes as input a collection of entities with corresponding textual descriptions as well as a set of descriptions for pairs of entities. The NUBBI model the produces a latent space description of both the entities and the relationships between them.
1 2  nubbi.collapsed.gibbs.sampler(contexts, pair.contexts, pairs, K.individual,
K.pair, vocab, num.iterations, alpha, eta, xi)

contexts 
The set of textual descriptions (i.e., documents) for individual
entities in LDA format (see

pair.contexts 
A set of textual descriptions for pairs of entities, also in LDA format. 
pairs 
Labelings as to which pair each element of 
K.individual 
A scalar integer representing the number of topics for the individual entities. 
K.pair 
A scalar integer representing the number of topics for entity pairs. 
vocab 
A character vector specifying the vocabulary words associated with the word indices used in contexts and pair.contexts. 
num.iterations 
The number of sweeps of Gibbs sampling over the entire corpus to make. 
alpha 
The scalar value of the Dirichlet hyperparameter for topic proportions. 
eta 
The scalar value of the Dirichlet hyperparamater for topic multinomials. 
xi 
The scalar value of the Dirichlet hyperparamater for source proportions. 
The NUBBI model is a switching model wherein the description of each entitypair can be ascribed to either the first entity of the pair, the second entity of the pair, or their relationship. The NUBBI model posits a latent space (i.e., topic model) over the individual entities, and a different latent space over entity relationships.
The collapsed Gibbs sampler used in this model is different than the variational inference method proposed in the paper and is highly experimental.
A fitted model as a list with the same components as returned by
lda.collapsed.gibbs.sampler
with the following additional components:
source_assignments 
A list of 
document_source_sums 
A matrix with three columns and

document_sums 
Semantically similar to the entry in

topics 
Like the entry in 
The underlying sampler is quite general and could potentially be used for other models such as the authortopic model (McCallum et al.) and the citation influence model (Dietz et al.). Please examine the source code and/or contact the author(s) for further details.
Jonathan Chang (slycoder@gmail.com)
Chang, Jonathan and BoydGraber, Jordan and Blei, David M. Connections between the lines: Augmenting social networks with text. KDD, 2009.
See lda.collapsed.gibbs.sampler
for a description of the
input formats and similar models.
rtm.collapsed.gibbs.sampler
is a different kind of
model for document networks.
1 2 3  ## See demo.
## Not run: demo(nubbi)

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