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- Title
DATA RETRIEVAL IN CANCER DOCUMENTS USING VARIOUS WEIGHTING SCHEMES.
- Authors
DANIEL, A. NICHOLAS; DEVI, JAYANTHILA
- Abstract
In the realm of data retrieval, sparse vectors serve as a pivotal representation for both documents and queries, where each element in the vector denotes a word or phrase from a predefined lexicon. In this study, multiple scoring mechanisms are introduced aimed at discerning the significance of specific terms within the context of a document extracted from an extensive textual dataset. Among these techniques, the widely employed method revolves around inverse document frequency (IDF) or Term Frequency-Inverse Document Frequency (TF-IDF), which emphasizes terms unique to a given context. Additionally, the integration of BM25 complements TF-IDF, sustaining its prevalent usage. However, a notable limitation of these approaches lies in their reliance on near-perfect matches for document retrieval. To address this issue, researchers have devised latent semantic analysis (LSA), wherein documents are densely represented as low-dimensional vectors. Through rigorous testing within a simulated environment, findings indicate a superior level of accuracy compared to preceding methodologies.
- Subjects
LATENT semantic analysis; INFORMATION retrieval; RESEARCH personnel
- Publication
I-Manager's Journal on Information Technology, 2023, Vol 12, Issue 4, p28
- ISSN
2277-5110
- Publication type
Article
- DOI
10.26634/jit.12.4.20365