Knowledge transformation from word space to document space
Tao Li, Chris H. Q. Ding, Yi Zhang, Bo Shao · 2008
In most IR clustering problems, we directly cluster the documents, working in the document space, using cosine similarity between documents as the similarity measure. In many real-world applica-tions, however, we usually have knowledge on the word side and wish to transform this knowledge to the document (concept) side. In this paper, we provide a mechanism for this knowledge trans-formation. To the best of our knowledge, this is the rst model for such type of knowledge transformation. This model uses a nonneg-ative matrix factorization model X = FSGT, where X is the word-document semantic matrix, F is the posterior probability of a word belonging to a word cluster and represents knowledge in the word space, G is the posterior probability of a document belonging to a document cluster and represents knowledge in the document space, and S is a scaled matrix factor which provides a condensed view of X. We show how knowledge on words can improve document clustering, i.e, knowledge in the word space is transformed into the document space. We perform extensive experiments to validate our approach.