In this paper, we explore the hypothesis that integrating symbolic top-down knowledge into text vector representations can improve neural exploratory bottom-up representations for text clustering. By extracting semantic rules from WordNet, terms with similar concepts are substituted with a more general term, the hypernym. This hypernym semantic relationship supplements the neural model in document clustering. The neural model is based on the extended significance vector representation approach into which predictive top-down knowledge is embedded. When we examine our hypothesis by six competitive neural models, the results are consistent and demonstrate that our robust hybrid neural approach is able to improve classification accuracy and reduce the average quantization error on 100,000 full-text articles.
['Chihli Hung', 'Stefan Wermter', 'Peter Smith']
Predictive Top-Down Knowledge Improves Neural Exploratory Bottom-Up Clustering