A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
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ECML '98 Proceedings of the 10th European Conference on Machine Learning
Distributional term representations: an experimental comparison
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TSD'07 Proceedings of the 10th international conference on Text, speech and dialogue
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CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
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Information Retrieval
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CICLing'10 Proceedings of the 11th international conference on Computational Linguistics and Intelligent Text Processing
A document is known by the company it keeps: neighborhood consensus for short text categorization
Language Resources and Evaluation
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Everyday, millions of short-texts are generated for which effective tools for organization and retrieval are required. Because of the tiny length of these documents and of their extremely sparse representations, the direct application of standard text categorization methods is not effective. In this work we propose using distributional term representations (DTRs) for short-text categorization. DTRs represent terms by means of contextual information, given by document occurrence and term co-occurrence statistics. Therefore, they allow us to develop enriched document representations that help to overcome, to some extent, the small-length and high-sparsity issues. We report experimental results in three challenging collections, using a variety of classification methods. These results show that the use of DTRs is beneficial for improving the classification performance of classifiers in short-text categorization.