Article
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Analyzing Syntactic Structural Dependency in NLP
Version 1
: Received: 5 December 2023 / Approved: 6 December 2023 / Online: 6 December 2023 (07:18:19 CET)
How to cite: Thomas, H.; Patel, R.; Dave, P. Analyzing Syntactic Structural Dependency in NLP. Preprints 2023, 2023120335. https://doi.org/10.20944/preprints202312.0335.v1 Thomas, H.; Patel, R.; Dave, P. Analyzing Syntactic Structural Dependency in NLP. Preprints 2023, 2023120335. https://doi.org/10.20944/preprints202312.0335.v1
Abstract
This paper introduces a novel graph polynomial approach for differentiating tree structures in dependency grammar. Utilizing this polynomial representation, we develop a metric to assess the similarity in syntax. This approach offers a detailed and inclusive analysis of the dependency structures and relationships in sentence construction. We employ this polynomial method to examine sentence structures across various languages in the Parallel Universal Dependencies treebanks. Our analysis includes comparing the syntax of original sentences and their translated counterparts in diverse languages, alongside a comprehensive study of syntactic typologies within these treebanks. Additionally, we explore the application of our methodology in evaluating the syntactic diversity within language corpora.
Keywords
Syntactic Analysis; Dependency Grammar; Linguistic Typology
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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