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Vectorial representations derived from large current events datasets such as Google News have been shown to perform well on word similarity tasks. This paper shows vectorial representations derived from substantially smaller explanatory text datasets such as English Wikipedia and Simple English Wikipedia preserve enough lexical semantic information to make these kinds of category judgments with equal or better accuracy. Analysis shows these results are driven by a prevalence of commonsense facts in explanatory text. These positive results for small datasets suggest vectors derived from slower but more accurate deep parsers may be practical for lexical semantic applications.
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