Intuitive demonstrations of Word2Vec like synonym generation and analogy tests provide compelling evidence that semantic representations are not only possible but also meaningful. While they may hold many opportunities for machine learning on text data, little work has gone into exploring texts on a smaller scale. If one is interested in comparing how texts use concepts in different contexts, small and contextually sparse corpuses may be sufficient. In this work I propose several methodological advancements for comparative semantic analysis and look at some of the biggest challenges that have yet to be addressed.
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