Knowledge discovery from scientific articles has received
increasing attention recently since huge repositories are made available by the
development of the Internet and digital databases. In a corpus of scientific
articles such as a digital library, documents are connected by citations and
one document plays two different roles in the corpus: document itself and a
citation of other documents. In the existing topic models, little effort is
made to differentiate these two roles. We believe that the topic distributions
of these two roles are different and related in a certain way. In this paper,
we propose a Bernoulli process topic (BPT) model which considers the corpus at
two levels: document level and citation level. In the BPT model, each document
has two different representations in the latent topic space associated with its
roles. Moreover, the multi-level hierarchical structure of citation network is
captured by a generative process involving a Bernoulli process. The
distribution parameters of the BPT model are estimated by a variation
approximation approach. An efficient computation algorithm is proposed to
overcome the difficulty of matrix inverse operation. In addition to conducting
the experimental evaluations on the document modeling and document clustering
tasks, we also apply the BPT model to well known corpora to discover the latent
topics, recommend important citations, detect the trends of various research
areas in computer science between 1991 and 1998, and to investigate the
interactions among the research areas. The comparisons against state-of-the-art
methods demonstrate a very promising performance. The implementations and the
data sets are available online.
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