A diachronic thesaurus is a lexical resource that aims to map between modern terms and their semantically related terms in earlier periods. In this paper, we investigate the task of collecting a list of relevant modern target terms for a domain-specific diachronic thesaurus. We propose a supervised learning scheme, which integrates features from two closely related fields: Terminology Extraction and Query Performance Prediction (QPP). Our method further expands modern candidate terms with ancient related terms, before assessing their corpus relevancy with QPP measures. We evaluate the empirical benefit of our method for a thesaurus for a diachronic Jewish corpus.
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