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Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation., , , , and . AACL/IJCNLP, page 224-234. Association for Computational Linguistics, (2020)UDLex: Towards Cross-language Subcategorization Lexicons., , and . DepLing, page 207-217. Linköping University Electronic Press, (2017)Creation and Use of Lexicons and Ontologies for NL Interfaces to Databases., , , , , , , and . LREC, page 219-224. European Language Resources Association (ELRA), (2006)Nine Features in a Random Forest to Learn Taxonomical Semantic Relations., , , , and . LREC, European Language Resources Association (ELRA), (2016)Computational Models of Language Meaning in Context (Dagstuhl Seminar 13462)., , and . Dagstuhl Reports, 3 (11): 79-116 (2013)Same same but different: Type and typicality in a distributional model of complement coercion, , and . Word Structure and Word Usage. Proceedings of the NetWordS Final Conference. Pisa, March 30-April 1, 2015, page 91--94. Pisa, Italy, (2015)Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?, , , and . IWCS(1), The Association for Computer Linguistics, (2017)Fitting, Not Clashing! A Distributional Semantic Model of Logical Metonymy., , , and . IWCS, page 404-410. The Association for Computer Linguistics, (2013)ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms., , , , and . AAAI, page 4262-4263. AAAI Press, (2016)Are Language Models Sensitive to Semantic Attraction? A Study on Surprisal., , , and . *SEM@ACL, page 141-148. Association for Computational Linguistics, (2023)