  In this paper, we provide a novel way to generate low dimensional vector
embeddings for the noun and verb synsets in WordNet, where the hypernym-hyponym
relationship is preserved in the embeddings. We call this embedding the Sense
Spectrum (and Sense Spectra for embeddings). In order to create suitable labels
for the training of sense spectra, we designed a new similarity measurement for
noun and verb synsets in WordNet. We call this similarity measurement the
Hypernym Intersection Similarity (HIS), since it compares the common and unique
hypernyms between two synsets. Our experiments show that on the noun and verb
pairs of the SimLex-999 dataset, HIS outperforms the three similarity
measurements in WordNet. Moreover, to the best of our knowledge, the sense
spectra provide the first dense synset embeddings that preserve the semantic
relationships in WordNet.
