Text Classification Using Association Rules, Dependency Pruning and Hyperonymization

Autor:	Haralambous, Yannis, Lenca, Philippe
Rok vydání:	2014
Předmět:	Computer Science - Information Retrieval Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: 16 pages, 2 figures, presented at DMNLP 2014
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1407.7357 Zobrazit plný text záznamu View this record from Arxiv