Text Classification Using Association Rules, Dependency Pruning and Hyperonymization
arXiv:1407.7357v126 citations
Originality Incremental advance
AI Analysis
This work addresses text classification efficiency for NLP applications, but it appears incremental as it builds on existing association rule mining techniques.
The paper tackled text classification by introducing pruning methods based on dependency syntax and enhancing methods using hyperonymization to improve association rule mining, resulting in performance gains compared to tfidf-based pruning.
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.