  There are many scenarios where we may want to find pairs of textually similar
documents in a large corpus (e.g. a researcher doing literature review, or an
R&D project manager analyzing project proposals). To programmatically discover
those connections can help experts to achieve those goals, but brute-force
pairwise comparisons are not computationally adequate when the size of the
document corpus is too large. Some algorithms in the literature divide the
search space into regions containing potentially similar documents, which are
later processed separately from the rest in order to reduce the number of pairs
compared. However, this kind of unsupervised methods still incur in high
temporal costs. In this paper, we present an approach that relies on the
results of a topic modeling algorithm over the documents in a collection, as a
means to identify smaller subsets of documents where the similarity function
can then be computed. This approach has proved to obtain promising results when
identifying similar documents in the domain of scientific publications. We have
compared our approach against state of the art clustering techniques and with
different configurations for the topic modeling algorithm. Results suggest that
our approach outperforms (> 0.5) the other analyzed techniques in terms of
efficiency.
