  Domain-specific synonyms occur in many specialized search tasks, such as when
searching medical documents, legal documents, and software engineering
artifacts. We replicate prior work on ranking domain-specific synonyms in the
consumer health domain by applying the approach to a new language and domain:
identifying Swedish language synonyms in the building construction domain. We
chose this setting because identifying synonyms in this domain is helpful for
downstream systems, where different users may query for documents (e.g.,
engineering requirements) using different terminology. We consider two new
features inspired by the change in language and methodological advances since
the prior work's publication. An evaluation using data from the building
construction domain supports the finding from the prior work that synonym
discovery is best approached as a learning to rank task in which a human editor
views ranked synonym candidates in order to construct a domain-specific
thesaurus. We additionally find that FastText embeddings alone provide a strong
baseline, though they do not perform as well as the strongest learning to rank
method. Finally, we analyze the performance of individual features and the
differences in the domains.
