Structured distributional semantic models aim to improve upon simple vector space models of semantics by hypothesizing that the meaning of a word is captured more effectively through its relational \u2014 rather than its raw distributional \u2014 signature. In accordance, they extend the vector space paradigm by structuring elements with relational information that decompose distributional signatures over discrete relation dimensions. However, the number and nature of these relations remains an open research question, with most previous work in the literature employing syntactic dependencies as surrogates for truly semantic relations. In this paper we propose a novel structured distributional semantic model with latent relation dimensions, and instantiate it using latent relational analysis. Evaluation of our model yields results that significantly outperform several other distributional approaches on two semantic tasks and performs competitively on a third relation classification task.<\/p>\n","protected":false},"excerpt":{"rendered":"
Structured distributional semantic models aim to improve upon simple vector space models of semantics by hypothesizing that the meaning of a word is captured more effectively through its relational \u2014 rather than its raw distributional \u2014 signature. In accordance, they extend the vector space paradigm by structuring elements with relational information that decompose distributional signatures 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