  We seek to better understand the difference in quality of the several
publicly released embeddings. We propose several tasks that help to distinguish
the characteristics of different embeddings. Our evaluation of sentiment
polarity and synonym/antonym relations shows that embeddings are able to
capture surprisingly nuanced semantics even in the absence of sentence
structure. Moreover, benchmarking the embeddings shows great variance in
quality and characteristics of the semantics captured by the tested embeddings.
Finally, we show the impact of varying the number of dimensions and the
resolution of each dimension on the effective useful features captured by the
embedding space. Our contributions highlight the importance of embeddings for
NLP tasks and the effect of their quality on the final results.
