Experiments on the difference between semantic similarity and relatedness

Recent work has pointed out the difference between the concepts of semantic similarity and semantic relatedness. Importantly, some NLP applications depend on measures of semantic similarity, while others work better with measures of semantic relatedness. It has also been observed that methods of computing similarity measures from text corpora produce word spaces that are biased towards either semantic similarity or relatedness. Despite these findings, there has been little work that evaluates the effect of various techniques and parameter settings in the word space construction from corpora. The present paper experimentally investigates how the choice of context, corpus preprocessing and size, and dimension reduction techniques like singular value decomposition and frequency cutoffs influence the semantic properties of the resulting word spaces.