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Article type: Research Article
Authors: Mezzi, Melyaraa; * | Benblidia, Nadjiaa | Huang, Xiangjib
Affiliations: [a] LRDSI Laboratory, Computer Science Department, Saad Dahlab University, Blida 1, Blida, Algeria | [b] School of Information Technology, York University, Toronto, Ontario, Canada
Correspondence: [*] Corresponding author. Email: mezzi.melyara@gmail.com. Tel: (+213)792-351-842.
Abstract: In this paper, we propose a modified version of the Context-aware Stemming algorithm, itself based on the well-known Porter stemmer in an effort to maximize the proportion of the meaningful stems and thus, the search effectiveness without compromising the other performance measures. Several stemmers are presented and a synergetic hybrid solution is proposed. Indeed, the Semantically Enriched Context-Aware Stemming algorithm combines features from algorithmic stemmers and dictionary stemmers with respect to conceptual indexing techniques in order to improve retrieval performance; proposing root words much comparable to lemma. Moreover, a new query-document mapping technique is proposed based on a previous work and the experimental results conducted with the WT2G dataset show that our algorithm is noticeably more efficient as compared to Porter and CAS algorithms.
Keywords: Context-aware Stemmer, stemming, porter stemmer, context-awareness, semantic similarity
DOI: 10.3233/jid-2016-0028
Journal: Journal of Integrated Design and Process Science, vol. 21, no. 4, pp. 5-24, 2017
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