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1
Finding Concept-specific Biases in Form--Meaning Associations ...
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2
Systematic Inequalities in Language Technology Performance across the World's Languages ...
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3
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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5
Modeling the Unigram Distribution ...
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6
Finding Concept-specific Biases in Form–Meaning Associations ...
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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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8
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)
Abstract: The mapping of lexical meanings to wordforms is a major feature of natural languages. While usage pressures might assign short words to frequent meanings (Zipf’s law of abbreviation), the need for a productive and open-ended vocabulary, local constraints on sequences of symbols, and various other factors all shape the lexicons of the world’s languages. Despite their importance in shaping lexical structure, the relative contributions of these factors have not been fully quantified. Taking a coding-theoretic view of the lexicon and making use of a novel generative statistical model, we define upper bounds for the compressibility of the lexicon under various constraints. Examining corpora from 7 typologically diverse languages, we use those upper bounds to quantify the lexicon’s optimality and to explore the relative costs of major constraints on natural codes. We find that (compositional) morphology and graphotactics can sufficiently account for most of the complexity of natural codes—as measured by code length.
URL: https://doi.org/10.3929/ethz-b-000518982
https://hdl.handle.net/20.500.11850/518982
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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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