Greedy Texts Similarity Mapping

Abstract

The documents similarity metric is a substantial tool applied in areas such as determining topic in relation to documents, plagiarism detection, or problems necessary to capture the semantic, syntactic, or structural similarity of texts. Evaluated results of the similarity measure depend on the types of word represented and the problem statement and can be time-consuming. In this paper, we present a problem-independent algorithm of the similarity metric greedy texts similarity mapping (GTSM), which is computationally efficient to be applied for large datasets with any preferred word vectorization models. GTSM maps words in two texts based on a decision rule that evaluates word similarity and their importance to the texts. We compare it with the well-known word mover’s distance (WMD) algorithm in the k-nearest neighbors text classification problem and find that it leads to similar or better results. In the correlation evaluation task of similarity measures with human-judged scores, we demonstrate its higher correlation scores in comparison with WMD and sentence mover’s similarity (SMS) and show that GTSM is a decent alternative for both word-level and sentence-level tasks.

More by Irina Ualiyeva

• Detecting AI-Generated User Reviews in Kazakh: A Study on BERT Model Performance and False Positive ReductionLecture notes in computer science · 2026
• BENCHMARKING DEEP LEARNING MODELS FOR FEW-SHOT CLASSIFICATION OF KAZAKH NEWS TEXTS UNDER LIMITED ANNOTATIONHerald of Kazakh-British technical university · 2026
• Parallel Memetic Differential Evolution for Minimum Sum-of-Squares ClusteringCommunications in computer and information science · 2025
• COMPARATIVE ANALYSIS OF THE EFFECTIVENESS OF TRANSFORMER AND CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR AUTOMATIC CLASSIFICATION OF RICE LEAF DISEASESHerald of Kazakh-British technical university · 2025