  Word senses are not static and may have temporal, spatial or corpus-specific
scopes. Identifying such scopes might benefit the existing WSD systems largely.
In this paper, while studying corpus specific word senses, we adapt three
existing predominant and novel-sense discovery algorithms to identify these
corpus-specific senses. We make use of text data available in the form of
millions of digitized books and newspaper archives as two different sources of
corpora and propose automated methods to identify corpus-specific word senses
at various time points. We conduct an extensive and thorough human judgment
experiment to rigorously evaluate and compare the performance of these
approaches. Post adaptation, the output of the three algorithms are in the same
format and the accuracy results are also comparable, with roughly 45-60% of the
reported corpus-specific senses being judged as genuine.
