Abstract
The explosive growth of resources stored in various forms and transmitted over the internet has necessitated researches into information retrieval technologies. The major information retrieval mechanisms commonly employed include vector space model, Boolean model, Fuzzy Set model, and probabilistic retrieval model. These models are used to find similarities between the query and the documents to retrieve documents that reflect the query. These approaches are based on key-word, which uses lists of keywords to describe the information content. In this paper, a survey of these models is provided in order to understand their working mechanisms and shortcomings. This understanding is vital as it facilitates the choice of an information retrieval technique, based on the underlying requirements. The results of this survey revealed that the current information retrieval models fall short of the expectations in one way or the other. As such, they are not ideal for high precision information retrieval applications.
Highlights
The information retrieval strategies transform documents into suitable representations so that relevant documents can be retrieved effectively
The results obtained showed that the proposed model outperformed the state of the art models by significantly large margin
As [30] pointed out, various setbacks are inherent in the probabilistic model: it does not categorize documents based on their relevance; does not consider document weighing in building the index term and freedom of supposition for the index term; generally, the idea of probabilistic model is within a probabilistic scope, which allows the user to retrieve and which documents are relevant and which documents are not
Summary
To cite this article: Mang’are Fridah Nyamisa, Waweru Mwangi, Wilson Cheruiyot.
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