  Compounding is a highly productive word-formation process in some languages
that is often problematic for natural language processing applications. In this
paper, we investigate whether distributional semantics in the form of word
embeddings can enable a deeper, i.e., more knowledge-rich, processing of
compounds than the standard string-based methods. We present an unsupervised
approach that exploits regularities in the semantic vector space (based on
analogies such as "bookshop is to shop as bookshelf is to shelf") to produce
compound analyses of high quality. A subsequent compound splitting algorithm
based on these analyses is highly effective, particularly for ambiguous
compounds. German to English machine translation experiments show that this
semantic analogy-based compound splitter leads to better translations than a
commonly used frequency-based method.
