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Information Retrieval System Using BM25, Pivoted Normalization and Combsum methods

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dc.contributor.author Khaing, Nu Yin
dc.date.accessioned 2021-06-11T05:35:53Z
dc.date.available 2021-06-11T05:35:53Z
dc.date.issued 2021-06
dc.identifier.uri https://onlineresource.ucsy.edu.mm/handle/123456789/2585
dc.description.abstract Nowadays, a lot of information is available for someone who wants to find documents on Google or in a system or in a digital library. Information retrieval is important as a way that can solve problem by giving the information that seems to be related to documents. Retrieving information is difficult and time consuming for searching a variety and large number of documents on the digital library. The proposed system is intended to implement an effective searching system that intends to implement effective search system for digital library. BM25 and Pivoted Normalization are the best retrieval models for information retrieval system. The CombSUM is combining these two methods to get more relevant documents and to give better output results. The proposed system can help the user to get all relevant documents (conference papers) according to the given query. When a user enters the query, the most relevant documents are ranked by using BM25, Pivoted Normalization Method and CombSUM. en_US
dc.language.iso en en_US
dc.publisher University of Computer Studies, Yangon en_US
dc.title Information Retrieval System Using BM25, Pivoted Normalization and Combsum methods en_US
dc.type Thesis en_US

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