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The article presents Harbsafe-162, a domain-specific data set for evaluatingdistributional semantic models. It originates from a cooperation by TechnischeUniversit\"at Braunschweig and the German Commission for Electrical, Electronic& Information Technologies of DIN and VDE, the Harbsafe project. One objectiveof the project is to apply distributional semantic models to terminologicalentries, that is, complex lexical data comprising of at least one or severalterms, term phrases and a definition. This application is needed to solve amore complex problem: the harmonization of terminologies of standards andstandards bodies (i.e. resolution of doublettes and inconsistencies). Due to alack of evaluation data sets for terminological entries, the creation ofHarbsafe-162 was a necessary step towards harmonization assistance.Harbsafe-162 covers data from nine electrotechnical standards in the domain offunctional safety, IT security, and dependability. An intrinsic evaluationmethod in the form of a similarity rating task has been applied in which twolinguists and three domain experts from standardization participated. The dataset is used to evaluate a specific implementation of an established sentenceembedding model. This implementation proves to be satisfactory for thedomain-specific data so that further implementations for harmonizationassistance may be brought forward by the project. Considering recent criticismon intrinsic evaluation methods, the article concludes with an evaluation ofHarbsafe-162 and joins a more general discussion about the nature of similarityrating tasks. Harbsafe-162 has been made available for the community.

Harbsafe-162. A Domain-Specific Data Set for the Intrinsic Evaluation of Semantic Representations for Terminological Data