Synonym Discovery with Etymology-based Word Embeddings
This method addresses synonym discovery for languages with logographic writing systems, offering an alternative to corpus-based embeddings, though it is incremental as it adapts existing embedding concepts to etymological data.
The paper tackled synonym discovery by proposing a novel approach that learns word embeddings using etymological composition instead of context, requiring no large text corpus but reliable etymological data, and demonstrated its effectiveness on Chinese and Sino-Korean vocabularies with sets of 117,000 and 135,000 words, respectively.
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.