Cluster Based Symbolic Representation for Skewed Text Categorization
This addresses skewed text categorization, a common issue in NLP, but is incremental as it builds on clustering and symbolic methods.
The paper tackles the problem of imbalanced text corpora by converting them into balanced ones using clustering and symbolic representation, achieving superior performance on Reuters 21578 and TDT2 datasets compared to existing models like SVM.
In this work, a problem associated with imbalanced text corpora is addressed. A method of converting an imbalanced text corpus into a balanced one is presented. The presented method employs a clustering algorithm for conversion. Initially to avoid curse of dimensionality, an effective representation scheme based on term class relevancy measure is adapted, which drastically reduces the dimension to the number of classes in the corpus. Subsequently, the samples of larger sized classes are grouped into a number of subclasses of smaller sizes to make the entire corpus balanced. Each subclass is then given a single symbolic vector representation by the use of interval valued features. This symbolic representation in addition to being compact helps in reducing the space requirement and also the classification time. The proposed model has been empirically demonstrated for its superiority on bench marking datasets viz., Reuters 21578 and TDT2. Further, it has been compared against several other existing contemporary models including model based on support vector machine. The comparative analysis indicates that the proposed model outperforms the other existing models.