Word-Graph Construction Techniques for Context Analysis

Authors

  • Rafique Yasir PhD Scholar, School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
  • Wu Jue Professor, School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang, China.
  • Mushtaq Muhammad Umer PhD Scholar, School of Information Engineering, Southwest University of Science and Technology, Mianyang, China.
  • Atif Nazma MPhil Scholar, School of Information Engineering, Southwest University of Science and Technology, Mianyang, China.

DOI:

https://doi.org/10.5281/zenodo.10594263

Keywords:

Lexical Network, Graph Based Language Representation, Node Link Structure, Citation Index, Resemblance

Abstract

A Nomo-Word Graph Construction Analysis Method (NWGC-AM) is used to graph let the corresponding construction phrases into essential and non-essential citation groups. NMCS-NR, or Nomo Maximum Common Sub-graph edge resemblance, Maximum Common Subgraph Directed Edge resemblance (MCS-DER), and Maximum Common Subgraph Resemblance. The graph resemblance metrics used in this work are called Undirected Edges Resemblance (MCS-UER). The tests included five distinct classifiers: Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), Decision Trees, and Support Vector Machines (SVM).Four sixty one (361) citations made up the annotated dataset used for the studies.  The Decision Tree classifier exhibits superior performance, attaining an accuracy rate of 0.98.

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Published

2024-01-06

How to Cite

Rafique Yasir, Wu Jue, Mushtaq Muhammad Umer, & Atif Nazma. (2024). Word-Graph Construction Techniques for Context Analysis. LC International Journal of STEM (ISSN: 2708-7123), 4(4), 25-35. https://doi.org/10.5281/zenodo.10594263