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German in Flux: Detecting Metaphoric Change via Word Entropy

, , , , and . Proceedings of the 21st Conference on Computational Natural Language Learning, page 354--367. Vancouver, Canada, (2017)

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Predicting Degrees of Technicality in Automatic Terminology Extraction, , , and . Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, page 2883--2889. Online, Association for Computational Linguistics, (July 2020)CCOHA: Clean Corpus of Historical American English, , , and . Proceedings of The 12th Language Resources and Evaluation Conference, page 6958--6966. Marseille, France, European Language Resources Association, (May 2020)Hypernyms under Siege: Linguistically-motivated Artillery for Hypernymy Detection., , and . EACL (1), page 65-75. Association for Computational Linguistics, (2017)SURel: A Gold Standard for Incorporating Meaning Shifts into Term Extraction., , and . *SEM@NAACL-HLT, page 1-8. Association for Computational Linguistics, (2019)A Wind of Change : Detecting and Evaluating Lexical Semantic Change across Times and Domains, , , and . The 57th Annual Meeting of the Association for Computational Linguistics - proceedings of the conference, page 732-746. Stroudsburg, PA, Association for Computational Linguistics, (2019)Effects of Pre- and Post-Processing on type-based Embeddings in Lexical Semantic Change Detection, , , and . Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics : Main Volume, page 125-137. Stroudsburg, Association for Computational Linguistics, (2021)A Wind of Change: Detecting and Evaluating Lexical Semantic Change across Times and Domains, , , and . Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, page 732--746. Florence, Italy, Association for Computational Linguistics, (2019)Simulating Lexical Semantic Change from Sense-Annotated Data, and . The Evolution of Language: Proceedings of the 13th International Conference (EvoLang13), (2020)IMS at SemEval-2020 Task 1: How low can you go? Dimensionality in Lexical Semantic Change Detection, , , and . Proceedings of the 14th International Workshop on Semantic Evaluation, Barcelona, Spain, Association for Computational Linguistics, (2020)CL-IMS @ DIACR-Ita: Volente o Nolente: BERT does not outperform SGNS on Semantic Change Detection, , , , and . Proceedings of the 7th evaluation campaign of Natural Language Processing and Speech tools for Italian (EVALITA 2020), Online, CEUR.org, (2020)