N-gram Based Lexical Sentence Similarity Score Using Modified Jaccard Algorithm
DOI:
https://doi.org/10.70112/ajcst-2025.14.2.4389Keywords:
Natural Language Processing (NLP), Sentence Similarity, Answer Assessment, Jaccard Algorithm, Subjective Answer EvaluationAbstract
In recent years, the Internet has evolved into a global phenomenon, making it nearly impossible to envision modern life without it. Among the vast forms of online content, textual data holds the greatest significance due to its abundance and informational value. However, managing and analysing such extensive text corpora poses several challenges, with sentence similarity emerging as one of the most complex problems in Natural Language Processing (NLP). Although existing sentence comparison techniques perform effectively in specific contexts, they often struggle in others and typically require substantial computational resources, including powerful hardware, extensive training datasets, and high processing capabilities. To address these limitations, this study introduces a lightweight approach that emphasizes word-level similarity through comprehensive n-gram comparisons. The proposed method incorporates semantic understanding and evaluates the longest common subsequence within sentences to generate a more accurate similarity score. It demonstrates superior efficiency over baseline methods by minimizing computational requirements and leveraging straightforward mathematical operations.
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