Dependency Distance as a Metric of Language Comprehension Difficulty

Liu, Hai Tao

doi:10.17791/jcs.2008.9.2.159

Cited by 244 publications

(98 citation statements)

References 30 publications

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“…For each pair of words linked by a dependency relation, the one is called the dependent and the other the governor . The labeled arc extends from the governor to the dependent (Liu, 2008). The directed edge from governor to dependent illustrates the asymmetrical relation between these two units.…”

Section: Methodsmentioning

confidence: 99%

“…This tendency is found across different languages (Liu, 2008; Futrell et al, 2015), genres (Wang and Liu, 2017), and also code-switching discourses (Wang and Liu, 2013), suggesting that it is impacted by external constraints, especially that of limited working memory. This impact has been established in a succession of empirical investigations in various languages (Gibson, 1998; Hsiao and Gibson, 2003; Grodner and Gibson, 2005).…”

Section: Introductionmentioning

confidence: 97%

“…The seconds possibility could be the opposite of the first one, for in SI, language production is highly constrained by the input, and hence the interference in syntactic structures between two languages, mainly from source language to target language, may significantly impact the MDD of the output speech. Prior research suggests that the MDD of Chinese is higher than that of English (Liu, 2008; Jiang and Liu, 2015; Liu et al, 2017), and hence it is very likely that DD of SI can be higher than CI.…”

Section: Introductionmentioning

confidence: 99%

“…Given that DD mirrors constraints on human cognition (Liu, 2008; Jiang and Liu, 2015; Liu et al, 2017), it is very likely that the extreme cognitive demand of interpreting makes the DD of CI and SI shorter than that of TR. However, the difference in DD between CI and SI could entail two contrasting possibilities.…”

Section: Introductionmentioning

confidence: 99%

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Dependency Distance Differences across Interpreting Types: Implications for Cognitive Demand

Liang

Fang

et al. 2017

Front. Psychol.

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Interpreting is generally recognized as a particularly demanding language processing task for the cognitive system. Dependency distance, the linear distance between two syntactically related words in a sentence, is an index of sentence complexity and is also able to reflect the cognitive constraints during various tasks. In the current research, we examine the difference in dependency distance among three interpreting types, namely, simultaneous interpreting, consecutive interpreting and read-out translated speech based on a treebank comprising these types of interpreting output texts with dependency annotation. Results show that different interpreting renditions yield different dependency distances, and consecutive interpreting texts entail the smallest dependency distance other than those of simultaneous interpreting and read-out translated speech, suggesting that consecutive interpreting bears heavier cognitive demands than simultaneous interpreting. The current research suggests for the first time that interpreting is an extremely demanding cognitive task that can further mediate the dependency distance of output sentences. Such findings may be due to the minimization of dependency distance under cognitive constraints.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dependency Distance Differences across Interpreting Types: Implications for Cognitive Demand

Liang

Fang

et al. 2017

Front. Psychol.

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“…Sentence structure is the form, and semantics is the content. [7] The results of syntactic analysis is the basic of semantic analysis. In the system, the language environment is so single, simple semantic analysis is enough in some time.…”

Section: B Analysis Limited Natural Language Sentence Structure and mentioning

confidence: 99%

Notice of Retraction Research on conversion technology of abstract logic structure diagram to implementation diagram

Yao

Liu

2011

2011 Seventh International Conference on Natural Computation

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Developing programs faster and efficiently has been computer worker's pursuit goal for a long time. Procedure Blueprint is a visual process modeling language to develop programs conveniently, which makes up the imperfection of Unified Modeling Language in process description and can ensure the consistency of analysis, design and structure products in program development. What inadequate in the existing Procedure Blueprint is that concept, logic and implementation layer information need to manual input. Which not only exist many repeated manual labours, but also hinders the efficient program development seriously. This paper proposes a method, which is based on limited natural language process and module mapping, to realize the semi-auto conversion form logic layer to implementation layer of Procedure Blueprint. Applying this method to improve the existing Procedure Blueprint tool, on the one hand the tool keeps up the advantage of unifing the program development stages, on the other hand improves the efficiency and quality of program development.

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Can chunking reduce syntactic complexity of natural languages?

2016

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PACS 89.75.Hc -Networks and genealogical trees PACS 05.40.Fb -Random walks and Levy flights PACS 89.90.+n -Other topics in areas of applied and interdisciplinary physics Abstract -This paper hypothesizes that chunking plays important role in reducing dependency distance and dependency crossings. Computer simulations, when compared with natural languages， show that chunking reduces mean dependency distance (MDD) of a linear sequence of nodes (constrained by continuity or projectivity) to that of natural languages. More interestingly, chunking alone brings about less dependency crossings as well, though having failed to reduce them, to such rarity as found in human languages. These results suggest that chunking may play a vital role in the minimization of dependency distance, and a somewhat contributing role in the rarity of dependency crossing. In addition, the results point to a possibility that the rarity of dependency crossings is not a mere side-effect of minimization of dependency distance, but a linguistic phenomenon with its own motivations.Introduction. -Language used in communication is invariably presented linearly, one unit after another, which is regarded as one of its fundamental property [1]. However, there is always a sytactic tree structure underlying a onedimensional linear sentence, a structure underpinning both the production and the comprehension of this sentence [2,3]. Therefore, language processing consists, to a considerable degree, in the transformation between the syntactic tree structure and the one-dimensional linear arrangement. What properties can be found in the tree structure of language? What mechanisms constrain the transformation of tree structure into linear structure? The answers to these questions, which may well require researches based on statistical physics and computer simulation, probably will shed much light on how human language operates.In terms of dependency grammar, the structure of a sentence can be visualized as a hierarchical dependency tree, whose nodes (vertices) are words, linked to one another by directed edges (dependency relations) [2,3]. Such a hierarchical tree must be ultimately arranged into a linear sequence, for the purpose of spoken and written communication. So far, researches have repeatedly observed two phenomena in the linear realization of hierarchical dependency structure: the minimization of dependency distance (the number of intervening words) between two syntactically related words [4][5][6][7][8][9][10][11][12][13], and the rarity of crossing

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Dependency Distance as a Metric of Language Comprehension Difficulty

Cited by 244 publications

References 30 publications

Dependency Distance Differences across Interpreting Types: Implications for Cognitive Demand

Dependency Distance Differences across Interpreting Types: Implications for Cognitive Demand

Notice of Retraction Research on conversion technology of abstract logic structure diagram to implementation diagram

Can chunking reduce syntactic complexity of natural languages?

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