Text classification using association rules, dependency pruning and hyperonymization
Y Haralambous, P Lenca - arXiv preprint arXiv:1407.7357, 2014 - arxiv.org
arXiv preprint arXiv:1407.7357, 2014•arxiv.org
We present new methods for pruning and enhancing item-sets for text classification via
association rule mining. Pruning methods are based on dependency syntax and enhancing
methods are based on replacing words by their hyperonyms of various orders. We discuss
the impact of these methods, compared to pruning based on tfidf rank of words.
association rule mining. Pruning methods are based on dependency syntax and enhancing
methods are based on replacing words by their hyperonyms of various orders. We discuss
the impact of these methods, compared to pruning based on tfidf rank of words.
We present new methods for pruning and enhancing item- sets for text classification via association rule mining. Pruning methods are based on dependency syntax and enhancing methods are based on replacing words by their hyperonyms of various orders. We discuss the impact of these methods, compared to pruning based on tfidf rank of words.
arxiv.org
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