Optimal one-page tree embeddings in linear time

Hochberg, Robert; Stallmann, Matthias F.

doi:10.1016/s0020-0190(03)00261-8

Cited by 17 publications

(30 citation statements)

References 7 publications

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“…In this example, A has two dependents, and minimal dependency length is achieved by placing these two dependents on opposite sides of the head. This prediction of DLM was derived by Gildea & Temperley (2007, and presaged by work on similar problems in abstract graph theory (Hochberg & Stallmann 2003). Gildea & Temperley (2007) found that the optimal strategy to achieve minimal dependency length (while maintaining projectivity) is to place dependents on alternating sides of their head outward in order of increasing length.…”

Section: Short-before-long and Long-before-short Constituent Ordering...mentioning

confidence: 93%

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Dependency locality as an explanatory principle for word order

Futrell¹,

Lévy²,

Gibson³

2020

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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, providing a rich set of explananda for future linguistic theories.

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Section: Short-before-long and Long-before-short Constituent Ordering...mentioning

confidence: 93%

“…While DLM favors projective word orders, it is not the case that minimal-dependency-length word orders are necessarily projective. It is in some cases possible to achieve lower dependency lengths by breaking projectivity (Chung 1984;Hochberg & Stallmann 2003;Park & Levy 2009).…”

Section: What Can Dependency Locality Explain?mentioning

confidence: 99%

Dependency locality as an explanatory principle for word order

Futrell¹,

Lévy²,

Gibson³

2020

Language

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“…reduces crossings to practically zero [7], this does not provide a full explanation about the low frequency of crossings in real sentences: (a) minimum D does not imply C = 0 [11], (b) the actual value of D in real sentences is located between the minimum and that of a random ordering of vertices [12] and (c) the word order that minimizes D might be in a serious conflict with other linguistic or cognitive constraints [13]. Here the problem of the reduction of D that is required for explaining C ≈ 0 in real sentences is avoided by means of a null hypothesis that predicts C by considering the actual length of the edges that may cross.…”

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confidence: 99%

A stronger null hypothesis for crossing dependencies

Ferrer-i-Cancho¹

2014

EPL

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PACS 89.75.Hc -Networks and genealogical trees PACS 89.75.Fb -Structures and organization in complex systems PACS 05.40.Fb -Random walks and Levy flightsAbstract -The syntactic structure of a sentence can be modeled as a tree where vertices are words and edges indicate syntactic dependencies between words. It is well-known that those edges normally do not cross when drawn over the sentence. Here a new null hypothesis for the number of edge crossings of a sentence is presented. That null hypothesis takes into account the length of the pair of edges that may cross and predicts the relative number of crossings in random trees with a small error, suggesting that a ban of crossings or a principle of minimization of crossings are not needed in general to explain the origins of non-crossing dependencies. Our work paves the way for more powerful null hypotheses to investigate the origins of non-crossing dependencies in nature.

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“…It has been shown that a linear arrangement of vertices with crossings can achieve a smaller value of D than that of a minimum linear arrangement that minimizes D when no crossings are allowed (Hochberg and Stallmann 2003) - Fig. It has been shown that a linear arrangement of vertices with crossings can achieve a smaller value of D than that of a minimum linear arrangement that minimizes D when no crossings are allowed (Hochberg and Stallmann 2003) - Fig.…”

Section: The Relationship Between Minimization Of Crossings and Minimmentioning

confidence: 99%

Towards a Theoretical Framework for Analyzing Complex Linguistic Networks

Mehler¹,

Lcking²,

Banisch³

et al. 2016

Understanding Complex Systems

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Complexity is an interdisciplinary program publishing the best research and academiclevel teaching on both fundamental and applied aspects of complex systems-cutting across all traditional disciplines of the natural and life sciences, engineering, economics, medicine, neuroscience, social and computer science.Complex Systems are systems that comprise many interacting parts with the ability to generate a new quality of macroscopic collective behavior the manifestations of which are the spontaneous formation of distinctive temporal, spatial or functional structures. Models of such systems can be successfully mapped onto quite diverse "real-life" situations like the climate, the coherent emission of light from lasers, chemical reaction-diffusion systems, biological cellular networks, the dynamics of stock markets and of the internet, earthquake statistics and prediction, freeway traffic, the human brain, or the formation of opinions in social systems, to name just some of the popular applications.Although their scope and methodologies overlap somewhat, one can distinguish the following main concepts and tools: self-organization, nonlinear dynamics, synergetics, turbulence, dynamical systems, catastrophes, instabilities, stochastic processes, chaos, graphs and networks, cellular automata, adaptive systems, genetic algorithms and computational intelligence.The three major book publication platforms of the Springer Complexity program are the monograph series "Understanding Complex Systems" focusing on the various applications of complexity, and the "Springer Series in Synergetics", which is devoted to the quantitative theoretical and methodological foundations, and the "SpringerBriefs in Complexity" which are concise and topical working reports, case-studies, surveys, essays and lecture notes of relevance to the field. In addition to the books in these two core series, the program also incorporates individual titles ranging from textbooks to major reference works.Future scientific and technological developments in many fields will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition-typically many different kinds of components interacting simultaneously and nonlinearly with each other and their environments on multiple levels-and in the rich diversity of behavior of which they are capable.The Springer Series in Understanding Complex Systems series (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate the concepts, methods and tools of complex systems at all levels of description and in all scientific fields, especially newly emerging areas within the life, social, behavioral, economic, neuro-and cognitive sciences (and derivatives thereof); second, to encourage novel applications of these ideas in various fields of engineering and computation such as robotics, nano-technology...

show abstract

Optimal one-page tree embeddings in linear time

Cited by 17 publications

References 7 publications

Dependency locality as an explanatory principle for word order

Dependency locality as an explanatory principle for word order

A stronger null hypothesis for crossing dependencies

Towards a Theoretical Framework for Analyzing Complex Linguistic Networks

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