Hybrid neural document clustering using guided self-organization and WordNet

Document clustering is text processing that groups documents with similar concepts. It´s usually considered an unsupervised learning approach because there´s no teacher to guide the training process, and topical information is often assumed to be unavailable. A guided approach to document clustering that integrates linguistic top-down knowledge from WordNet into text vector representations based on the extended significance vector weighting technique improves both classification accuracy and average quantization error. In our guided self-organization approach we integrate topical and semantic information from WordNet. Because a document-training set with preclassified information implies relationships between a word and its preference class, we propose a novel document vector representation approach to extract these relationships for document clustering. Furthermore, merging statistical methods, competitive neural models, and semantic relationships from symbolic Word-Net, our hybrid learning approach is robust and scales up to a real-world task of clustering 100,000 news documents.