Redefining Context Windows for Word Embedding Models: An Experimental Study

Understand

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.