  Automatic Term Extraction deals with the extraction of terminology from a
domain specific corpus, and has long been an established research area in data
and knowledge acquisition. ATE remains a challenging task as it is known that
there is no existing ATE methods that can consistently outperform others in any
domain. This work adopts a refreshed perspective to this problem: instead of
searching for such a 'one-size-fit-all' solution that may never exist, we
propose to develop generic methods to 'enhance' existing ATE methods. We
introduce SemRe-Rank, the first method based on this principle, to incorporate
semantic relatedness - an often overlooked venue - into an existing ATE method
to further improve its performance. SemRe-Rank incorporates word embeddings
into a personalised PageRank process to compute 'semantic importance' scores
for candidate terms from a graph of semantically related words (nodes), which
are then used to revise the scores of candidate terms computed by a base ATE
algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on
four datasets of diverse nature, it is shown to have achieved widespread
improvement over all base methods and across all datasets, with up to 15
percentage points when measured by the Precision in the top ranked K candidate
terms (the average for a set of K's), or up to 28 percentage points in F1
measured at a K that equals to the expected real terms in the candidates (F1 in
short). Compared to an alternative approach built on the well-known TextRank
algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision
at top K, or up to 17 points in F1.
