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Sound–meaning association biases evidenced across thousands of languages

, , , , and . Proceedings of the National Academy of Sciences, 113 (39): 10818--10823 (September 2016)
DOI: 10.1073/pnas.1605782113

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Speakers Fill Lexical Semantic Gaps with Context., , , and . EMNLP (1), page 4004-4015. Association for Computational Linguistics, (2020)On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs., , , , and . CoRR, (2020)Is Word Segmentation Child's Play in All Languages?, , , , and . ACL (1), page 3931-3937. Association for Computational Linguistics, (2019)On the interpretation and significance of bias metrics in texts: a PMI-based approach., , , , and . CoRR, (2021)Finding Concept-specific Biases in Form-Meaning Associations., , , , and . NAACL-HLT, page 4416-4425. Association for Computational Linguistics, (2021)Modeling infant segmentation of two morphologically diverse languages., , , and . CORIA-TALN-RJC (TALN), page 47-60. ATALA, (2018)Quantifying the Semantic Core of Gender Systems., , , , and . EMNLP/IJCNLP (1), page 5733-5738. Association for Computational Linguistics, (2019)Meaning to Form: Measuring Systematicity as Information., , , , and . ACL (1), page 1751-1764. Association for Computational Linguistics, (2019)Evaluating Word Embeddings with Categorical Modularity., , and . ACL/IJCNLP (Findings), volume ACL/IJCNLP 2021 of Findings of ACL, page 1982-1993. Association for Computational Linguistics, (2021)Sound–meaning association biases evidenced across thousands of languages, , , , and . Proceedings of the National Academy of Sciences, 113 (39): 10818--10823 (September 2016)