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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/23599

Title: Unraveling Bi-Lingual Multi-feature based Text Classification: A case study
Authors: Yarahmadi, Aziz
Creemers, Mathijs
Qabbaah, Hamzah
Vanhoof, Koen
Issue Date: 2017
Citation: International Journal Information Theories and Applications (Print), 24(1), p. 3-19
Abstract: Extracting knowledge out of unstructured text has attracted many experts in both academia and business sectors like media, logistics, telecommunication and production. In this context, classification techniques are increasing the potential of Natural Language Processing in order to produce an efficient application of text classification in business context. This method could extract patterns from desirable text. The main objective of this paper is implementing a classification system which can be widely applied in commercial product classification problem solving. We have employed various applications of Natural Language Processing and Data Mining in order to solve parcel classification problem. Furthermore, we have investigated a popular case study which is associated with parcel shipping companies all around the world. The proposed methodology in this paper is part of a supervised machine learning project undertaken in order to gain domain specific knowledge from text.
URI: http://hdl.handle.net/1942/23599
ISSN: 1310-0513
Category: A1
Type: Journal Contribution
Validation: vabb, 2019
Appears in Collections: Research publications

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