Dereje Teferi (PhD)Zelalem Addis2025-08-052025-08-052014-02https://etd.aau.edu.et/handle/123456789/6034This research has been conducted, aiming at augmenting the precision while lessening the original recall of an Amharic IR system. The main reason for performing query expansion is to provide relevant document as per users query that can satisfy their information domain area. They mostly formulate weak queries to retrieve documents. Thus, they end up frustrated with to the results found from an IR system. Some of the causes for this type of problem are, polysemous and synonymous terms, which require an integration of query reformulation strategy to the IR system. The present study has explored term re-weighting based query expansion approaches that integrate term re-weighting with Statistical Co-occurrence analysis, bi-gram analysis and bi-gram thesaurus methods. In this approach, the users relevance feedback are represented as vector respectively, and the similarity between them can be obtained by calculating the vector similarity. Then we re-weight the terms through one single document and the entire document set using Rocchios reweighting scheme respectively final weight of the term can be selected as query expansion terms and then fed to the three query term, regardless of their position. Term re-weighting, three proposed query expansion techniques were integrated to an information retrieval system. Then test result showed that bi-gram method outperformed the other two and scored 2% improvement in total F-measure. The performance of the system can further be improved by designing ontology based query expansion in order to control expanding terms that are polysemous by themselves.enInformation Retrievalpolysemous termsTerm re-weightingQuery ExpansionBi-gramThesaurusStatistical Co-occurrenceThesis