Dependency Structures Derived from Minimalist Grammars

Boston, Marisa Ferrara; Hale, John; Kuhlmann, Marco

doi:10.1007/978-3-642-14322-9_1

Cited by 15 publications

(17 citation statements)

References 14 publications

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“…2). While syntactic formalisms vary, the dependency grammar community has an agreed representation format that has been used to annotate corpora of text from dozens of languages (40), and there are computational methods for deriving such representations from other standard linguistic formalisms (41).…”

Section: Grammars and Grammar Datamentioning

confidence: 99%

Universals of word order reflect optimization of grammars for efficient communication

Hahn

Jurafsky

Futrell

2020

Proc. Natl. Acad. Sci. U.S.A.

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The universal properties of human languages have been the subject of intense study across the language sciences. We report computational and corpus evidence for the hypothesis that a prominent subset of these universal properties—those related to word order—result from a process of optimization for efficient communication among humans, trading off the need to reduce complexity with the need to reduce ambiguity. We formalize these two pressures with information-theoretic and neural-network models of complexity and ambiguity and simulate grammars with optimized word-order parameters on large-scale data from 51 languages. Evolution of grammars toward efficiency results in word-order patterns that predict a large subset of the major word-order correlations across languages.

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Section: Grammars and Grammar Datamentioning

confidence: 99%

Universals of word order reflect optimization of grammars for efficient communication

Hahn

Jurafsky

Futrell

2020

Proc. Natl. Acad. Sci. U.S.A.

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“…9 9 MGs are in the same complexity class as Tree Adjoining Grammar (Joshi, Levy, & Takahashi, 1975) and have a dependency interpretation (Boston, Hale, & Kuhlmann, 2010). …”

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confidence: 99%

Quantifying Structural and Non‐structural Expectations in Relative Clause Processing

Chen

Hale

2021

Cognitive Science

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Information‐theoretic complexity metrics, such as Surprisal (Hale, 2001; Levy, 2008) and Entropy Reduction (Hale, 2003), are linking hypotheses that bridge theorized expectations about sentences and observed processing difficulty in comprehension. These expectations can be viewed as syntactic derivations constrained by a grammar. However, this expectation‐based view is not limited to syntactic information alone. The present study combines structural and non‐structural information in unified models of word‐by‐word sentence processing difficulty. Using probabilistic minimalist grammars (Stabler, 1997), we extend expectation‐based models to include frequency information about noun phrase animacy. Entropy reductions derived from these grammars faithfully reflect the asymmetry between subject and object relatives (Staub, 2010; Staub, Dillon, & Clifton, 2017), as well as the effect of animacy on the measured difficulty profile (Lowder & Gordon, 2012; Traxler, Morris, & Seely, 2002). Visualizing probability distributions on the remaining alternatives at particular parser states allows us to explore new, linguistically plausible interpretations for the observed processing asymmetries, including the way that expectations about the relativized argument influence the processing of particular types of relative clauses (Wagers & Pendleton, 2016).

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“…However, we will use a dependency tree representation instead. Our dependency trees are merely a more compact encoding of MG derivation trees and have no connection to the MG dependency trees of Boston et al (2010). We will directly define MGs as sets of well-formed dependency trees, mirroring earlier definitions in terms of derivation trees (primarily Kobele et al 2007).…”

Section: Minimalist Grammarsmentioning

confidence: 99%

“…The central contribution of our paper is a uniform upper bound on syntax that encompasses both MG operations and licensing conditions. This upper bound takes the form of sensing tree automata (STAs) operating over dependency tree representations of MG derivations (these dependency trees are distinct from the MG dependency trees of Boston et al 2010). STAs provide a minimal amount of look-ahead to deterministic top-down tree automata: the automaton may inspect the labels of all daughter nodes before assigning them specific states.…”

Section: Introductionmentioning

confidence: 99%

Sensing Tree Automata as a Model of Syntactic Dependencies

Graf

Santo

2019

Proceedings of the 16th Meeting on the Mathematics of Language

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Various aspects of syntax have recently been characterized in subregular terms. However, these characterizations operate over very different representations, including string encodings of c-command relations as well as tiers projected from derivation trees. We present a way to unify these approaches via sensing tree automata over Minimalist grammar dependency trees. Sensing tree automata are deterministic top-down tree automata that may inspect the labels of all daughter nodes before assigning them specific states. It is already known that these automata cannot correctly enforce all movement dependencies in Minimalist grammars, but we show that this result no longer holds if one takes into account several well-established empirical restrictions on movement. Sensing tree automata thus furnish a strong yet uniform upper bound on the complexity of syntactic dependencies.

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Dependency Structures Derived from Minimalist Grammars

Cited by 15 publications

References 14 publications

Universals of word order reflect optimization of grammars for efficient communication

Universals of word order reflect optimization of grammars for efficient communication

Quantifying Structural and Non‐structural Expectations in Relative Clause Processing

Sensing Tree Automata as a Model of Syntactic Dependencies

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