A LITERATURE REVIEW: THE IMPORTANCE OF TERM NORMALIZATION IN VECTOR SPACE MODEL

Abstract

In today's digitally inundated era, accessing information is more accessible yet challenging due to the sheer volume available. This article underscores the pivotal role of VSM in managing vast data and enhancing retrieval accuracy by ranking documents based on query similarity. Term normalization, a part of VSM development, standardizes words for indexing, improving accuracy by addressing word variations. The study's methodology involved a systematic literature review, data collection via electronic databases, and thematic analysis. The research findings highlight vital aspects: the fundamentals of information retrieval systems, the working principle of VSM in document sorting, and the process of term normalization. Various methods within term normalization, such as tokenizing, filtering, stemming, and term weighting (e.g., TF, IDF, Cosine Similarity), are elucidated for refining document relevance. Discussions underscore the impact of term normalization on information retrieval, emphasizing heightened accuracy, efficiency, and reduced error rates. In the research paper, five studies that showcased successful applications of VSM across diverse domains were referenced. These domains included karaoke song searches, thesis examiner selection, pest identification in rice plants, hadith interpretation, and library material searches. Each study demonstrated the effectiveness and versatility of VSM in solving various problems in different fields. In conclusion, VSM emerges as a potent tool in managing information overload, particularly when coupled with normalization techniques. The studies reviewed illustrate VSM's efficacy in delivering precise results, affirming its status as a preferred method in information retrieval systems due to its accuracy and effectiveness.