Observations of word co-occurrences and similarity computations are often used as a straightforward way to represent the global contexts of words and achieve a simulation of semantic word similarity for applications such as word or document clustering and collocation extraction. Despite the simplicity of the underlying model, it is necessary to select a proper significance, a similarity measure and a similarity computation algorithm. However, it is often unclear how the measures are related and additionally often dimensionality reduction is applied to enable the efficient computation of the word similarity. This work presents a linear time complexity approximative algorithm for computing word similarity without any dimensionality reduction. It then introduces a large-scale evaluation based on two languages and two knowledge sources and discusses the underlying reasons for the relative performance of each measure.
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