Paper Info
Title
Quantitative Discourse Cohesion Analysis of Scientific Scholarly Texts Using Multilayer Networks
Abstract
Discourse cohesion facilitates text comprehension and helps the reader form a coherent narrative. In this study, we aim to computationally analyze the discourse cohesion in scientific scholarly texts using multilayer network representation and quantify the writing quality of the document. Exploiting the hierarchical structure of scientific scholarly texts, we design section-level and document-level metrics to assess the extent of lexical cohesion in text. We use a publicly available dataset along with a curated set of contrasting examples to validate the proposed metrics by comparing them against select indices computed using existing cohesion analysis tools. We observe that the proposed metrics correlate as expected with the existing cohesion indices. We also present an analytical framework, CHIAA (CHeck It Again, Author), to provide pointers to the author for potential improvements in the manuscript with the help of the section-level and document-level metrics. The proposed CHIAA framework furnishes a clear and precise prescription to the author for improving writing by localizing regions in text with cohesion gaps. We demonstrate the efficacy of CHIAA framework using succinct examples from cohesion-deficient text excerpts in the experimental dataset.
Year
DOI
Venue
2022
10.1109/ACCESS.2022.3198952
IEEE ACCESS
Keywords
DocType
Volume
Writing, Nonhomogeneous media, Measurement, Coherence, Semantics, Linguistics, Syntactics, Quality assessment, Computational modeling, Cohesion metrics, computational discourse analysis, discourse cohesion, multilayer networks, writing quality
Journal
10
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Vasudha Bhatnagar118117.69
Swagata Duari222.08
S. K. Gupta300.34