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Concepts with the same meanings will increase the weights of their synonyms. However, the text document is short and concepts are rarely repeated; therefore, we capture the semantic relationships among concepts and solve the disambiguation problem. The experimental results show that the proposed CBER is valuable in annotating short text documents to their best labels (classes). We used precision and recall measures to evaluate the proposed approach. CBER performance reached 93% and 94% in precision and recall, respectively.<\/jats:p>","DOI":"10.1515\/jisys-2015-0066","type":"journal-article","created":{"date-parts":[[2016,2,29]],"date-time":"2016-02-29T12:00:50Z","timestamp":1456747250000},"page":"233-241","source":"Crossref","is-referenced-by-count":0,"title":["CBER: An Effective Classification Approach Based on Enrichment Representation for Short Text Documents"],"prefix":"10.1515","volume":"26","author":[{"given":"Eman","family":"Ismail","sequence":"first","affiliation":[{"name":"Faculty of Computers and Information Sciences, Ain Shams University, Abbassia, Cairo, Egypt"}]},{"given":"Walaa","family":"Gad","sequence":"additional","affiliation":[{"name":"Faculty of Computers and Information Sciences, Ain Shams University, Abbassia, Cairo, Egypt"}]}],"member":"374","published-online":{"date-parts":[[2016,2,29]]},"reference":[{"key":"2025120523270262861_j_jisys-2015-0066_ref_001_w2aab3b7d177b1b6b1ab2ab1Aa","doi-asserted-by":"crossref","unstructured":"A. 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