585 research outputs found
Integrating Document Clustering and Topic Modeling
Document clustering and topic modeling are two closely related tasks which
can mutually benefit each other. Topic modeling can project documents into a
topic space which facilitates effective document clustering. Cluster labels
discovered by document clustering can be incorporated into topic models to
extract local topics specific to each cluster and global topics shared by all
clusters. In this paper, we propose a multi-grain clustering topic model
(MGCTM) which integrates document clustering and topic modeling into a unified
framework and jointly performs the two tasks to achieve the overall best
performance. Our model tightly couples two components: a mixture component used
for discovering latent groups in document collection and a topic model
component used for mining multi-grain topics including local topics specific to
each cluster and global topics shared across clusters.We employ variational
inference to approximate the posterior of hidden variables and learn model
parameters. Experiments on two datasets demonstrate the effectiveness of our
model.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
From the User to the Medium: Neural Profiling Across Web Communities
Online communities provide a unique way for individuals to access information
from those in similar circumstances, which can be critical for health
conditions that require daily and personalized management. As these groups and
topics often arise organically, identifying the types of topics discussed is
necessary to understand their needs. As well, these communities and people in
them can be quite diverse, and existing community detection methods have not
been extended towards evaluating these heterogeneities. This has been limited
as community detection methodologies have not focused on community detection
based on semantic relations between textual features of the user-generated
content. Thus here we develop an approach, NeuroCom, that optimally finds dense
groups of users as communities in a latent space inferred by neural
representation of published contents of users. By embedding of words and
messages, we show that NeuroCom demonstrates improved clustering and identifies
more nuanced discussion topics in contrast to other common unsupervised
learning approaches
Learning Behavioural Context
The original publication is available at www.springerlink.co
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