| Téléchargement | - Voir le manuscrit accepté : Text categorization for an online tendering system (PDF, 573 Kio)
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| Auteur | Rechercher : Wang, Y.; Rechercher : Zhang, H.; Rechercher : Spencer, B.1; Rechercher : Yan, Y.1 |
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| Affiliation | - Conseil national de recherches du Canada. Institut de technologie de l'information du CNRC
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| Format | Texte, Article |
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| Conférence | Business Agents and Semantic Web Workshop (BASeWEB 04), May 2004, London, Canada |
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| Sujet | text categorization; machine learning; Rocchio method; TF-IDF; WIDF; weighted inverse document frequency; naive Bayes classifier; ranking categorization |
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| Résumé | This paper investigates the application of text categoriza- tion (TC) in a setting exhibiting a large number of target categories with relatively few training cases, applied to a real-life online tendering system. This is an experiment paper showing our experiences in dealing with a real- life application using the conventional machine learning approaches for TC, namely, the Rocchio method, TF-IDF (term frequency-inverse document fre- quency), WIDF (weighted inverse document frequency), and naijve Bayes. In order to make the categorization results acceptable for industrial use, we made use of the hierarchical structure of the target categories and investi- gated the semi-automated ranking categorization. |
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| Date de publication | 2004-05-01 |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Numéro NPARC | 21260516 |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | a3b8b396-a184-43b9-b229-d47c4e95ed5c |
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| Enregistrement créé | 2013-03-05 |
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| Enregistrement modifié | 2020-06-04 |
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