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    Computer Science Department, Politehnica University of Bucharest
    Research Institute for Artificial Intelligence

    Autonomous News Clustering and Classification for an Intelligent Web Portal

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    The paper presents an autonomous text classification module for a news web portal for the Romanian language. Statistical natural language processing techniques are combined in order to achieve a completely autonomous functionality of the portal. The news items are automatically collected from a large number of news sources using web syndication. Afterward, machine-learning techniques are used for achieving an automatic classification of the news stream. Firstly, the items are clustered using an agglomerative algorithm and the resulting groups correspond to the main news topics. Thus, more information about each of the main topics is acquired from various news sources. Secondly, text classification algorithms are applied to automatically label each cluster of news items in a predetermined number of classes. More than a thousand news items were employed for both the training and the evaluation of the classifiers. The paper presents a complete comparison of the results obtained for each method.

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    Description

    Title : Autonomous News Clustering and Classification for an Intelligent Web Portal
    Author(s) : Traian Rebedea, Stefan Trausan-Matu
    Abstract : The paper presents an autonomous text classification module for a news web portal for the Romanian language. Statistical natural language processing techniques are combined in order to achieve a completely autonomous functionality of the portal. The news items are automatically collected from a large number of news sources using web syndication. Afterward, machine-learning techniques are used for achieving an automatic classification of the news stream. Firstly, the items are clustered using an agglomerative algorithm and the resulting groups correspond to the main news topics. Thus, more information about each of the main topics is acquired from various news sources. Secondly, text classification algorithms are applied to automatically label each cluster of news items in a predetermined number of classes. More than a thousand news items were employed for both the training and the evaluation of the classifiers. The paper presents a complete comparison of the results obtained for each method.
    Subject : unspecified
    Area : Other
    Year : 2008

    Affiliations Computer Science Department, Politehnica University of Bucharest
    Editors : Aijun An, Stan Matwin, Zbigniew W. Raś, Dominik Ślęzak
    Volume : 4994
    Publisher : Springer Berlin Heidelberg
    City : Berlin, Heidelberg
    Pages : 477 - 486
    Url : http://www.springerlink.com/index/10.1007/978-3-540-68123-6_52
    Isbn : 978-3-540-68122-9
    Doi : 10.1007/978-3-540-68123-6_52

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    Stefan's Peer Evaluation activity

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