A Probabilistic Information Retrieval System For Afan Oromo Text

Abstract

This thesis presents a research work on a probabilistic information retrieval system for Afan Oromo text. The primary purpose of an information retrieval system is to retrieve all the relevant documents, which are relevant to the user query. Information retrieval is not being an optional technology; it is an important to everybody and mandatory to use. As considerable amount of information is being produced in Afan Oromo rapidly and continuously; experimenting on the applicability of information retrieval system for Afan Oromo is important. The main objective of this study is to design a prototype architecture of Afan Oromo text retrieval system based on probabilistic model in order to increase its effectiveness in retrieving relevant documents as per the users information need. A Probabilistic retrieval model that has the capability of reweighting query terms based on relevance feedback could be used and also the potential of the model was investigated. The study presents the design and implementation of a probabilistic model for Afan Oromo free-text-documents. Both indexing and searching modules were constructed. Text operations were applied in both modules. Then, the retrieval system was tested using two hundred (200) Afan Oromo free-text-documents and ten (10) queries. Other types of documents like video, images and audio were not included. The development platform used to develop the system prototype is Python 3.6.5 programming language. The experimental results show that probabilistic based IR system in Afan Oromo free-text-documents returned encouraging result. The system registered, after user relevance feedback, an average precision, recall and Fmeasure of 60%, 91.56% and 72.5% respectively. This result is achieved without controlling the problem of synonyms and polysemous of terms that exist in Afan Oromo text. Though the performance of the system is greatly affected by the word variants, the result obtained is encouraging. It can be concluded that; when the terms are added to the user query and user relevance feedback is applied; the performance of the retrieval system increases. It is recommended that further research works be done to see the retrieval effectiveness of Afan Oromo IR system using other probabilistic models like bayesian network, Bayesian belief network, and Bayesian inference network model.