Abstract
Due to the boundless expansion of the Web in the last decade, the research community has paid significant attention to the problem of effective searching in the vast information available. In this paper, we introduce a novel framework for improving information retrieval results. Initially, relevant documents are organized in clusters utilizing several metrics combined with language modelling tools. In following, a produced ranked list of the documents is returned to the user for a specific query. This is implemented as the scores between the clusters and the query representations are extracted; next in line, the internal rankings of the documents, per cluster, using these scores as weighting factor, are combined. Our proposed methodology is based on the exploitation of the inter-documents similarities (lexical and/or semantics) after a sophisticated pre-processing step. Our experimental evaluation demonstrates that the proposed algorithm can efficiently improve the quality of the retrieved results.
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Notes
Google: https://www.google.com/search/about/.
A snippet is usually a short text summarizing the context in which the query words appear in the result page.
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Kanavos, A., Kotoula, P., Makris, C. et al. Employing query disambiguation using clustering techniques. Evolving Systems 11, 305–315 (2020). https://doi.org/10.1007/s12530-019-09292-7
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DOI: https://doi.org/10.1007/s12530-019-09292-7