Abstract
the rapid growth of information in the digital world especially on the web,calls for automated
methods of organizing the digital information for convenient access and efficient
information retrieval. Topic modeling is a branch of machine learning and probabilistic
graphical modeling that helps in arranging the web pages according to their topical
structure.
The topic distribution over a set of documents (web pages) and the affinity of
a document toward a specific topic can be revealed using topic modeling. Topic modeling
algorithms are typically computationally expensive due to their iterative nature.
Recent
research efforts have attempted to parallelize specific topic models and are successful in
their attempts.
These parallel algorithms however have tightly-coupled parallel processes
which require frequent synchronization and are also tightly coupled with the underlying
topic model which is used for inferring the topic hierarchy.