Quantifying syntax similarity with a polynomial representation of dependency trees

Liu, Pengyu; Feng, Tinghao; Liu, Rui

doi:10.53482/2022_53_402

Cited by 8 publications

(14 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, we can also apply the polynomials to other labeled, semi-labeled or unlabeled tree representations of RNA secondary structures. See [15] for an example of a complete polynomial invariant for labeled trees. RNA molecules fold into secondary structures that can be described by paired and unpaired nucleotides.…”

Section: Discussionmentioning

confidence: 99%

“…In [13], the author introduced a polynomial that assigns every unlabeled tree a unique polynomial representation; we refer to it as polynomial P . Recent studies have used methods based on polynomial P to analyze the evolution of pathogens [14] and the syntax of languages [15]. Here, we use polynomial P and rooted tree representations to study RNA secondary structures.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing the link between RNA secondary structures and R-loop formation with tree polynomials

Liu,

Lusk,

Jonoska

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

R-loops are a class of non-canonical nucleic acid structures that typically form during transcription, when the nascent RNA hybridizes with the DNA template strand, leaving the DNA coding strand unpaired. Co-transcriptional R-loops are abundant in nature and biologically relevant. Recent research shows that DNA sequence and topology affect R-loops, and yet it remains unclear how these and other factors drive R-loop formation. In this work, we investigate the link between the secondary structure of the nascent RNA and the probability of R-loop formation. We introduce tree-polynomial representations, a mathematical object that enables accurate and efficient data analysis of RNA secondary structures. With tree-polynomial representations, we establish a strong correlation between the secondary structure of the RNA transcript and the probability of R-loop formation. We identify that branches with short stems separated by multiple bubbles in the RNA secondary structure drive the strong correlation and potentially promote R-loop formation.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing the link between RNA secondary structures and R-loop formation with tree polynomials

Liu,

Lusk,

Jonoska

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…MDD is used as a new measurement for differentiating human languages. The studies related to MDD are mainly about the decreasing tendency of MDD in human languages which is a universal phenomenon (Futrell et al, 2015;Liu et al, 2016). The quantitative research of dependency distance is mainly related to the study of dependency direction.…”

Section: Dependency Distancementioning

confidence: 99%

Dependency Features of English Nouns in China News English

Li¹,

Cao²

2023

View full text Add to dashboard Cite

Nouns are one of the most frequently used word classes in English. As one variety of "World Englishes", China English has received much concern in recent years. However, most research on nouns is still at the cognitive aspect or in the traditional qualitative way. As a new quantitative method, dependency grammar reveals the internal relations among the words in the sentence. Therefore, the syntactic features of nouns in China News English can provide a new perspective. The raw material of China English in this study is randomly crawled from China Daily, and the News part in the Corpus of Contemporary American English (COCA) from 2013 to 2016is selected as the reference corpus. By using the Jupyter Notebook based on Python, the dependency treebanks are generated. After comparing the proportion of the word classes, dependency direction, and dependency distance of different dependents of nouns in China Daily and COCA, this paper finds that 1) On the whole, the categories of dependents in China English and American English are similar. Determiners, adjectives, nouns, prepositions, verbs, numbers, proper nouns, conjunctions, pronouns and particles are the top ten frequently-used modifiers of nouns. Compared with American English, China English tends to use more adjectives and nouns, while the determiners especially the articles and pronouns are relatively less used. 2) Both in China English and American English, the percentage of HI constructions and HF constructions is close to 50%. China English tends to be more right-embedded than American English. Compared with AmE, in ChiE, prepositions, conjunctions and participles are more frequently used as post-modifiers. 3) The mean dependency distance value of China English (2.68) is larger than American English (2.61). Both in China English and American English, the mean dependency distance of pre-modifiers is shorter than in post-modifiers. The longer the mean dependency distance value is, the more difficult is in processing the information. It indicates that in the News domain, China English is more difficult to be processed in expression. This research provides a new perspective to studying English variants and enriches the current research on nouns.

show abstract

“…The sentence is visualized as a hierarchical system with the predicate verb as its root. The dependency distance of a dependency pair is the linear distance between the governor and the dependent (Liu, 2007). See Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Dependency Distance Minimization (DDM) refers to the tendency to shorten the dependency distance in human languages (Ferrer-i-Cancho & Liu, 2014; Liu, 2007; Temperley, 2008). Large-scale cross-linguistic studies have confirmed that DDM is a global property of human languages (Futrell et al, 2015; Liu, 2008; Liu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Corpus-Based Study of the Dependency Distance Differences in English Academic Writing

Gao,

2023

Sage Open

View full text Add to dashboard Cite

Dependency distance has increasingly become a key measure of interest in cross-linguistic corpus studies from multiple perspectives. Based on a syntactically annotated corpus of 400 PhD dissertation abstracts written by native English (L1) and English as a foreign language (L2) academic writers, the current study investigated the mean dependency distance (MDD) variation across language backgrounds and disciplines, which is followed by a grammatical description based on fine-grained indices related to particular syntactic structures. The findings include: (1) L2 academic writers produce an averagely longer MDD than L1 academic writers because of their heavy use of prepositional phrases; (2) The MDD of the linguistics abstracts is significantly longer than that of the physics & chemistry abstracts because of the relatively higher syntactic complexity of the language of linguistics. The findings suggest that MDD can effectively differentiate academic texts with different language backgrounds and disciplines, that both L1 and L2 academic writers write under the constraint of dependency distance minimization, and that L2 PhD dissertation writers have achieved native-like writing proficiency in extending nominal structures.

show abstract

Quantifying syntax similarity with a polynomial representation of dependency trees

Cited by 8 publications

References 25 publications

Analyzing the link between RNA secondary structures and R-loop formation with tree polynomials

Analyzing the link between RNA secondary structures and R-loop formation with tree polynomials

Dependency Features of English Nouns in China News English

A Corpus-Based Study of the Dependency Distance Differences in English Academic Writing

Contact Info

Product

Resources

About