CC BY 4.0Tsereteli, TornikeTornikeTsereteliKartal, Yavuz SelimYavuz SelimKartalPonzetto, Simone PaolaSimone PaolaPonzettoZielinski, AndreaAndreaZielinskiEckert, KaiKaiEckertMayr, PhilippPhilippMayr2023-02-102023-02-102022https://doi.org/10.24406/h-428015https://publica.fraunhofer.de/handle/publica/42801510.24406/h-428015In this paper, we provide an overview of the SVIdent shared task as part of the 3rd Workshop on Scholarly Document Processing (SDP) at COLING 2022. In the shared task, participants were provided with a sentence and a vocabulary of variables, and asked to identify which variables, if any, are mentioned in individual sentences from scholarly documents in full text. Two teams made a total of 9 submissions to the shared task leaderboard. While none of the teams improve on the baseline systems, we still draw insights from their submissions. Furthermore, we provide a detailed evaluation. Data and baselines for our shared task are freely available at https://github.com/vadis-project/sv-ident.enSurvey variable detectionMachine learningSemantic textual similarityconference paper