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Finding Concept-specific Biases in Form--Meaning Associations ...
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Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models ...
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A surprisal--duration trade-off across and within the world's languages ...
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What About the Precedent: An Information-Theoretic Analysis of Common Law ...
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Finding Concept-specific Biases in Form–Meaning Associations ...
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How (Non-)Optimal is the Lexicon? ...
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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 ... : Tiago Pimentel and Irene Nikkarinen contributed equally to this work. Accepted at NAACL 2021. This is the camera ready version ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2104.14279 https://arxiv.org/abs/2104.14279
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Disambiguatory Signals are Stronger in Word-initial Positions ...
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Modeling the Unigram Distribution
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In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (2021)
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What About the Precedent: An Information-Theoretic Analysis of Common Law
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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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Finding Concept-specific Biases in Form–Meaning Associations
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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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