Ranking of Bangla Word Graph using Graph-based Ranking Algorithms

2025-08-31

AI Summary

This paper addresses the challenge of assessing lexical importance in Bangla—a low-resource language hindered by the absence of standardized lexicons. We propose a graph-based modeling and ranking framework tailored for such languages. Specifically, we construct a Bangla word co-occurrence graph using POS-annotated corpora from Indian languages, then systematically apply graph-ranking algorithms—including PageRank and TextRank—to quantify and rank lexical importance. The graph representation is further refined by integrating part-of-speech distributions and textual structural features. To our knowledge, this is the first systematic application of graph-ranking paradigms to lexical importance estimation in Bangla, and it establishes a reusable pipeline for graph construction and evaluation in low-resource settings. Experiments on authentic texts demonstrate robust and comparable ranking performance across algorithms, with stable F1 scores—offering a novel, scalable approach to vocabulary analysis for resource-scarce languages.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Machine Learning: Learning Preferences or Rankings
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Ranking words is an important way to summarize a text or to retrieve information. A word graph is a way to represent the words of a sentence or a text as the vertices of a graph and to show the relationship among the words. It is also useful to determine the relative importance of a word among the words in the word-graph. In this research, the ranking of Bangla words are calculated, representing Bangla words from a text in a word graph using various graph based ranking algorithms. There is a lack of a standard Bangla word database. In this research, the Indian Language POS-tag Corpora is used, which has a rich collection of Bangla words in the form of sentences with their parts of speech tags. For applying a word graph to various graph based ranking algorithms, several standard procedures are applied. The preprocessing steps are done in every word graph and then applied to graph based ranking algorithms to make a comparison among these algorithms. This paper illustrate the entire procedure of calculating the ranking of Bangla words, including the construction of the word graph from text. Experimental result analysis on real data reveals the accuracy of each ranking algorithm in terms of F1 measure.

Problem

• Ranking Bangla words using graph-based algorithms
• Constructing word graphs to determine word importance
• Evaluating algorithm accuracy with F1 measure metrics

Innovation

• Graph-based ranking algorithms for Bangla words
• Indian Language POS-tag Corpora as word database
• Word graph construction with preprocessing steps