Abstract
A contrastive approach to term extraction is an extensive class of methods based on the assumption that the words frequently occurring within a domain and rarely beyond it are most likely terms. The disadvantage of this approach is a great number of type II errors – false negatives. The cause of these errors is in the idea of contrastive selection when the most representative high frequent terms are extracted from the texts and rare terms are discarded. In this work, we propose a new operationalization of the contrastive approach, which supports the capture of both high frequent and low frequent domain terms. Proposed operationalization reduces the number of false negatives. The experiments performed on the texts of the subject domain “Geology” show promising of proposed approach.
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Nugumanova, A., Bessmertny, I., Baiburin, Y., Mansurova, M. (2016). A New Operationalization of Contrastive Term Extraction Approach Based on Recognition of Both Representative and Specific Terms. In: Ngonga Ngomo, AC., Křemen, P. (eds) Knowledge Engineering and Semantic Web. KESW 2016. Communications in Computer and Information Science, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-45880-9_9
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