Hybrid neural document clustering using guided self-organization and WordNet

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.

Abstract

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.