  In this paper, we propose an unsupervised method to identify noun sense
changes based on rigorous analysis of time-varying text data available in the
form of millions of digitized books. We construct distributional thesauri based
networks from data at different time points and cluster each of them separately
to obtain word-centric sense clusters corresponding to the different time
points. Subsequently, we compare these sense clusters of two different time
points to find if (i) there is birth of a new sense or (ii) if an older sense
has got split into more than one sense or (iii) if a newer sense has been
formed from the joining of older senses or (iv) if a particular sense has died.
We conduct a thorough evaluation of the proposed methodology both manually as
well as through comparison with WordNet. Manual evaluation indicates that the
algorithm could correctly identify 60.4% birth cases from a set of 48 randomly
picked samples and 57% split/join cases from a set of 21 randomly picked
samples. Remarkably, in 44% cases the birth of a novel sense is attested by
WordNet, while in 46% cases and 43% cases split and join are respectively
confirmed by WordNet. Our approach can be applied for lexicography, as well as
for applications like word sense disambiguation or semantic search.
