  We propose a novel approach to learn word embeddings based on an extended
version of the distributional hypothesis. Our model derives word embedding
vectors using the etymological composition of words, rather than the context in
which they appear. It has the strength of not requiring a large text corpus,
but instead it requires reliable access to etymological roots of words, making
it specially fit for languages with logographic writing systems. The model
consists on three steps: (1) building an etymological graph, which is a
bipartite network of words and etymological roots, (2) obtaining the
biadjacency matrix of the etymological graph and reducing its dimensionality,
(3) using columns/rows of the resulting matrices as embedding vectors. We test
our model in the Chinese and Sino-Korean vocabularies. Our graphs are formed by
a set of 117,000 Chinese words, and a set of 135,000 Sino-Korean words. In both
cases we show that our model performs well in the task of synonym discovery.
