C02-1063 central task for approaches to text classification or categorization . They require
C02-1087 networks including the SOM model for text classification apply VSM on their pre-processing
C02-1155 past , most of previous works on text classification focus on classifying text documents
C02-1087 classification . Introduction Text classification is the categorization of documents
C02-1155 multi-dimensional framework on text classification was proposed . The framework
C04-1115 used for similar problems such as text classification . We find that successful application
C02-1155 multidimensional framework , for text classification . The framework allows multiple
C02-1103 consists of a training phase and a text classification phase . The former includes the
C02-1087 hypernym relation from WordNet for text classification . We successfully used this relation
C02-1087 big difference in accuracy for a text classification task . Third , a trained SOM
C04-1092 to define the resources for a text classification system on terrorism . Most of
C04-1092 IE is at least as long as for text classification . To address this problem of
C02-1155 a statistical approach to our text classification in this work . For each document
C02-1087 this relation and improved the text classification performance substantially . By
C02-1087 used to verify the performance of text classification . A SOM map with 225 output units
C02-1087 WordNet is proposed to deal with the text classification of news articles . The remainder
C02-1155 Multi-Dimensional Category Model for Text Classification Category is a powerful tool to
C02-1155 recent works attempting to automate text classification based on this category hierarchy
C02-1155 classifier ( k-NN ) is applied to our text classification . First , the classifier calculates
C02-1087 and show a lot of potential for text classification . Introduction Text classification
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