@article{4763,
  author = {Pit Pichappan},
  title = {Comparative N-Gram Structure and Concept Transition Analysis in Polymer Journal: A Corpus-Based Investigation},
  journal = {Journal of Digital Information Management},
  year = {2026},
  volume = {24},
  number = {2},
  doi = {https://doi.org/10.6025/jdim/2026/24/2/97-119},
  url = {https://www.dline.info/fpaper/jdim/v24i2/jdimv24i2_3.pdf},
  abstract = {The exponential growth of scientific literature necessitates advanced computational methods to trace
conceptual evolution and semantic relationships. This study investigates N-gram structures and concepttransition
hierarchies within 96 research articles from the 2025 issues of the journal Polymers. Using NLPbased
corpus analysis, we evaluated lexical diversity, phrase density, and collocation strength at the unigram,
bigram, and trigram levels. Furthermore, we modelled concept expansion trees, semantic pathway networks,
and community structures, validating our findings through rigorous bootstrap and permutation testing.
The Results shown a progressive increase in lexical diversity and collocation strength from unigrams to
trigrams, confirming a systematic shift toward highly specialised, cohesive technical expressions. Core
foundational concepts such as "model" and "thermal" exhibit hierarchical expansion into complex trigrams
(e.g., "deep learning model"), highlighting the growing integration of computational intelligence into
traditional thermokinetics and materials research. Semantic network analysis reveals a scale free architecture
dominated by central hub concepts, while community detection identifies four distinct thematic clusters
that facilitate targeted interdisciplinary knowledge transfer.
Ultimately, the polymer literature corpus exhibits a mature, cumulative, and internally coherent knowledge
system. This integrated methodological approach provides a robust, statistically validated framework for
mapping the development of scientific terminology, disciplinary maturity, and interdisciplinary convergence
in rapidly evolving research domains, offering valuable insights for future Natural Language Processing.},
}