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The growth of domain-specific applications of semantic models, boosted by therecent achievements of unsupervised embedding learning algorithms, demandsdomain-specific evaluation datasets. In many cases, content-based recommendersbeing a prime example, these models are required to rank words or textsaccording to their semantic relatedness to a given concept, with particularfocus on top ranks. In this work, we give a threefold contribution to addressthese requirements: (i) we define a protocol for the construction, based onadaptive pairwise comparisons, of a relatedness-based evaluation datasettailored on the available resources and optimized to be particularly accuratein top-rank evaluation; (ii) we define appropriate metrics, extensions ofwell-known ranking correlation coefficients, to evaluate a semantic model viathe aforementioned dataset by taking into account the greater significance oftop ranks. Finally, (iii) we define a stochastic transitivity model to simulatesemantic-driven pairwise comparisons, which confirms the effectiveness of theproposed dataset construction protocol.

Top-Rank-Focused Adaptive Vote Collection for the Evaluation of Domain-Specific Semantic Models