  Distributional semantic models learn vector representations of words through
the contexts they occur in. Although the choice of context (which often takes
the form of a sliding window) has a direct influence on the resulting
embeddings, the exact role of this model component is still not fully
understood. This paper presents a systematic analysis of context windows based
on a set of four distinct hyper-parameters. We train continuous Skip-Gram
models on two English-language corpora for various combinations of these
hyper-parameters, and evaluate them on both lexical similarity and analogy
tasks. Notable experimental results are the positive impact of cross-sentential
contexts and the surprisingly good performance of right-context windows.
