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Dependency locality as an explanatory principle for word order
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In: Prof. Levy (2022)
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Abstract:
© 2020 Printed with the permission of Richard Futrell, Roger P. Levy, & Edward Gibson. This work focuses on explaining both grammatical universals of word order and quantitative word-order preferences in usage by means of a simple efficiency principle: dependency locality. In its simplest form, dependency locality holds that words linked in a syntactic dependency (any head–dependent relationship) should be close in linear order. We give large-scale corpus evidence that dependency locality predicts word order in both grammar and usage, beyond what would be expected from independently motivated principles, and demonstrate a means for dissociating grammar and usage in corpus studies. Finally, we discuss previously undocumented variation in dependency length and how it correlates with other linguistic features such as head direction, pro-viding a rich set of explananda for future linguistic theories.*.
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URL: https://hdl.handle.net/1721.1/138802.2
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| 2 |
Granularity in the Semantics of Comparison
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In: Semantics and Linguistic Theory; Proceedings of SALT 31; 550-569 ; 2163-5951 (2022)
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| 3 |
Competition from novel features drives scalar inferences in reference games
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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| 4 |
Using the Interpolated Maze Task to Assess Incremental Processing in English Relative Clauses
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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| 5 |
Child-directed Listening: How Caregiver Inference Enables Children's Early Verbal Communication
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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| 6 |
On Factors Influencing Typing Time: Insights from a Viral Online Typing Game
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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| 7 |
Eye Movement Traces of Linguistic Knowledge
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In: Proceedings of the Annual Meeting of the Cognitive Science Society, vol 43, iss 43 (2021)
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| 8 |
A Systematic Assessment of Syntactic Generalization in Neural Language Models
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In: Association for Computational Linguistics (2021)
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| 9 |
Hierarchical Representation in Neural Language Models: Suppression and Recovery of Expectations
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In: Association for Computational Linguistics (2021)
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| 10 |
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
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In: Association for Computational Linguistics (2021)
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| 11 |
Comparing Models of Associative Meaning: An Empirical Investigation of Reference in Simple Language Games
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In: Association for Computational Linguistics (2021)
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| 12 |
Cognitive Science Honors the Memory of Jeffrey Elman
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In: MIT Press (2021)
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| 13 |
Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization
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In: Association for Computational Linguistics (2021)
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| 14 |
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study
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In: Association for Computational Linguistics (2021)
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| 15 |
SyntaxGym: An Online Platform for Targeted Evaluation of Language Models
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In: Association for Computational Linguistics (2021)
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| 16 |
Structural Supervision Improves Few-Shot Learning and Syntactic Generalization in Neural Language Models
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In: Association for Computational Linguistics (2021)
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| 17 |
Neural language models as psycholinguistic subjects: Representations of syntactic state
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In: Association for Computational Linguistics (2021)
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| 18 |
Linking artificial and human neural representations of language
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In: Association for Computational Linguistics (2021)
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| 19 |
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies
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In: Association for Computational Linguistics (2021)
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| 20 |
Maze Made Easy: Better and easier measurement of incremental processing difficulty
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In: Other repository (2021)
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