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The early detection and classification of non-functional requirements (NFRs) is not only a hard and time consuming process, but also crucial in the evaluation of architectural alternatives starting from initial design decisions. In this paper, we propose a recommender system based on a semi-supervised learning approach for assisting analysts in the detection and classification of NFRs from textual requirements descriptions. Classification relies on a reduced number of categorized requirementsdoi:10.4114/ia.v13i44.1044 fatcat:ukwidini7zfi5iq4jqmz7eylha