  The increase in the number of researchers coupled with the ease of publishing
and distribution of scientific papers (due to technological advancements) has
resulted in a dramatic increase in astronomy literature. This has likely led to
the predicament that the body of the literature is too large for traditional
human consumption and that related and crucial knowledge is not discovered by
researchers. In addition to the increased production of astronomical
literature, recent decades have also brought several advancements in
computational linguistics. Especially, the machine-aided processing of
literature dissemination might make it possible to convert this stream of
papers into a coherent knowledge set. In this paper, we present the application
of computational linguistics techniques to astronomy literature. In particular,
we developed a tool that will find similar articles purely based on text
content from an input paper. We find that our technique performs robustly in
comparison with other tools recommending articles given a reference paper
(known as recommender system). Our novel tool shows the great power in
combining computational linguistics with astronomy literature and suggests that
additional research in this endeavor will likely produce even better tools that
will help researchers cope with the vast amounts of knowledge being produced.
