  This paper presents an ensemble part-of-speech tagging approach for source
code identifiers. Ensemble tagging is a technique that uses machine-learning
and the output from multiple part-of-speech taggers to annotate natural
language text at a higher quality than the part-of-speech taggers are able to
obtain independently. Our ensemble uses three state-of-the-art part-of-speech
taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble's
annotations on five different types of identifier names: function, class,
attribute, parameter, and declaration statement at the level of both individual
words and full identifier names. We also study and discuss the weaknesses of
our tagger to promote the future amelioration of these problems through further
research. Our results show that the ensemble achieves 75\% accuracy at the
identifier level and 84-86\% accuracy at the word level. This is an increase of
+17\% points at the identifier level from the closest independent
part-of-speech tagger.
