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1
Finding Concept-specific Biases in Form--Meaning Associations ...
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Systematic Inequalities in Language Technology Performance across the World's Languages ...
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Modeling the Unigram Distribution ...
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4
A surprisal--duration trade-off across and within the world's languages ...
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Modeling the Unigram Distribution ...
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Finding Concept-specific Biases in Form–Meaning Associations ...
Abstract: This work presents an information-theoretic operationalisation of cross-linguistic non-arbitrariness. It is not a new idea that there are small, cross-linguistic associations between the forms and meanings of words. For instance, it has been claimed (Blasi et al., 2016) that the word for “tongue” is more likely than chance to contain the phone [l]. By controlling for the influence of language family and geographic proximity within a very large concept-aligned, cross-lingual lexicon, we extend methods previously used to detect within language non-arbitrariness (Pimentel et al., 2019) to measure cross-linguistic associations. We find that there is a significant effect of non-arbitrariness, but it is unsurprisingly small (less than 0.5% on average according to our information-theoretic estimate). We also provide a concept-level analysis which shows that a quarter of the concepts considered in our work exhibit a significant level of cross-linguistic non-arbitrariness. In sum, the paper provides new methods to ... : Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies ...
URL: http://hdl.handle.net/20.500.11850/518985
https://dx.doi.org/10.3929/ethz-b-000518985
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7
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs ...
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9
How (Non-)Optimal is the Lexicon? ...
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10
Evaluating Word Embeddings with Categorical Modularity ...
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11
Modeling the Unigram Distribution
In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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12
Finding Concept-specific Biases in Form–Meaning Associations
In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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13
How (Non-)Optimal is the Lexicon?
In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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14
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs
In: Transactions of the Association for Computational Linguistics, 9 (2021)
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15
Evaluating Word Embeddings with Categorical Modularity ...
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16
How (Non-)Optimal is the Lexicon? ...
NAACL 2021 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021
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17
How (Non-)Optimal is the Lexicon? ...
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18
Finding Concept-specific Biases in Form--Meaning Associations ...
NAACL 2021 2021; Blasi, Damián; Cotterell, Ryan. - : Underline Science Inc., 2021
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19
Neural signatures of syntactic variation in speech planning
In: PLoS Biol (2021)
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20
Meaning before grammar: A review of ERP experiments on the neurodevelopmental origins of semantic processing [<Journal>]
Morgan, Elena Usai [Verfasser]; Meer, Audrey van der [Verfasser]; Vulchanova, Mila [Verfasser].
DNB Subject Category Language
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