Dependency Distance Motifs in 21 Indo- European Languages

Jing, Yingqi; Liu, Haitao

doi:10.1515/9783110476637-008

Cited by 4 publications

(4 citation statements)

References 12 publications

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“…So far, the empirical description of the statistics of motif sequences has been used for the comparison of authors (Biemann et al, 2016; Al Rozz and Menezes, 2018), texts (Chen, 2017), genres (Wang, 2017), languages (Chen and Liang, 2017; Jing and Liu, 2017; Mikros and Macutek, 2017), and for classification purposes (Köhler and Naumann, 2010; Liu and Liang, 2017). Given the correlation between word order and human cognitive functions (Dryer, 1992), the investigations into the language sequential units relative of function words distribution can provide a better illustration of the syntactic processing mechanisms attributing different interpreting styles and complement with the previous results based on grammatical annotations.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Interpreting Types: Language Sequence Mirrors Cognitive Load Minimization in Interpreting Tasks

Liang

Liu

2019

Front. Psychol.

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Most interpreting theories claim that different interpreting types should involve varied processing mechanisms and procedures. However, few studies have examined their underlying differences. Even though some previous results based on quantitative approaches show that different interpreting types yield outputs of varying lexical and syntactic features, the grammatical parsing approach is limited. Language sequences that form without relying on parsing or processing with a specific linguistic approach or grammar excel other quantitative approaches at revealing the sequential behavior of language production. As a non-grammatically-bound unit of language sequences, frequency motif can visualize the local distribution of content and function words, and can also statistically classify languages and identify text types. Thus, the current research investigates the distribution, length and position-dependent properties of frequency motifs across different interpreting outputs in pursuit of the sequential generation behaviors. It is found that the distribution, the length and certain position-dependent properties of the specific language sequences differ significantly across simultaneous interpreting and consecutive interpreting output. The features of frequency motifs manifest that both interpreting output is produced in the manner that abides by the least effort principle. The current research suggests that interpreting types can be differentiated through this type of language sequential unit and offers evidence for how the different task features mediate the sequential organization of interpreting output under different demand to achieve cognitive load minimization.

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Section: Introductionmentioning

confidence: 99%

Quantifying Interpreting Types: Language Sequence Mirrors Cognitive Load Minimization in Interpreting Tasks

Liang

Liu

2019

Front. Psychol.

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“…As the three correlation-related analyses all show a significant influence of article type on MDD and DDI, it could be claimed that on the whole there is an effect of native language transfer on the MDD and DDI of English L2 academic writing. Currently, no study has been found to examine the MDD and DDI of all languages in the world due to various reasons, though some studies have investigated the DDs of some languages, such as Chen and Gerdes (2020) , Jing and Liu (2017) , Futrell et al (2015) and Liu (2008) . However, examining studies such as Liu (2008) reveals a trend: the greater the MDD in the background language of English L2 academic writings, the longer the MDD tends to be in English L2 academic writings themselves.…”

Section: Resultsmentioning

confidence: 99%

Language transfer in L2 academic writings: a dependency grammar approach

Bi,

Tan

2024

Front. Psychol.

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Dependency distance (DD) is an important factor in language processing and can affect the ease with which a sentence is understood. Previous studies have investigated the role of DD in L2 writing, but little is known about how the native language influences DD in L2 academic writing. This study is probably the first one that investigates, though a large dataset of over 400 million words, whether the native language of L2 writers influences the DD in their academic writings. Using a dataset of over 2.2 million abstracts of articles downloaded from Scopus in the fields of Arts & Humanities and Social Sciences, the study analyzes the DD patterns, parsed by the latest version of the syntactic parser Stanford Corenlp 4.5.5, in the academic writing of L2 learners from different language backgrounds. It is found that native languages influence the DD of English L2 academic writings. When the mean dependency distance (MDD) of native languages is much longer than that of native English, the MDD of their English L2 academic writings will be much longer than that of English native academic writings. The findings of this study will deepen our insights into the influence of native language transfer on L2 academic writing, potentially shaping pedagogical strategies in L2 academic writing education.

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“…As the dependency treebanks are getting dominant, studies based on dependency grammar start to take lead. We can find recent studies discussing the relations between sentence lengths, tree heights, tree widths, and mean dependency distances (Jing & Liu, 2017;.…”

Section: The Relation Between Dependency Distance and Frequency 1 Intmentioning

confidence: 99%

Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)

2019

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The use of corpora for NLP and linguistics has only increased in recent years. In NLP, machine learning systems are by nature data-intensive, and in linguistics there is a renewed interest in the empirical validation of linguistic theory, particularly through corpus evidence. While the first statistical parsers have long been trained on the Penn treebank phrase structures, dependency treebanks, whether natively annotated with dependencies, or converted from phrase structures, have become more and more popular, as evidenced by the success of the Universal Dependency project, currently uniting 120 treebanks in 80 languages, annotated in the same dependency-based scheme. The availability of these resources has boosted empirical quantitative studies in syntax. It has also lead to a growing interest in theoretical questions around syntactic dependency, its history, its foundations, and the analyses of various constructions in dependency-based frameworks. Furthermore, the availability of large, multilingual annotated data sets, such as those provided by the Universal Dependencies project, has made cross-linguistic analysis possible to an extent that could only be dreamt of only a few years ago. In this context it was natural to bring together TLT (Treebanks and Linguistic Theories), the historical conference on treebanks as linguistic resources, Depling (The international conference on Dependency Linguistics), the conference uniting research on models and theories around dependency representations, and UDW (Universal Dependency Workshop), the annual meeting of the UD project itself. Moreover, in order to create a point of contact with the large community working in quantitative linguistics it seemed expedient to create a workshop dedicated to quantitative syntactic measures on treebanks and raw corpora, which gave rise to Quasy, the first workshop on Quantitative Syntax. And this led us to the first SyntaxFest. Because the potential audience and submissions to the four events were likely to have substantial overlap, we decided to have a single reviewing process for the whole SyntaxFest. Authors could choose to submit their paper to one or several of the four events, and in case of acceptance, the program co-chairs would decide which event to assign the accepted paper to.

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Dependency Distance Motifs in 21 Indo- European Languages

Cited by 4 publications

References 12 publications

Quantifying Interpreting Types: Language Sequence Mirrors Cognitive Load Minimization in Interpreting Tasks

Quantifying Interpreting Types: Language Sequence Mirrors Cognitive Load Minimization in Interpreting Tasks

Language transfer in L2 academic writings: a dependency grammar approach

Proceedings of the First Workshop on Quantitative Syntax (Quasy, SyntaxFest 2019)

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