Semantic document clustering is a type of unsupervised learning in which documents are grouped together based on their meaning. Unlike traditional approaches that cluster documents based on common keywords, this technique can group documents that share no words in common as long as they are on the same subject. We compute the similarity between two documents as a function of the semantic similarity between the words and phrases in the documents. We model information from WordNet and DBPedia as a probabilistic graph that can be used to compute the similarity between two terms. We experimentally validate our algorithm on the Reuters-21578 benchmark, which contains 11,362 newswire stories that are grouped in 82 categories using human judgment. We apply the k-means clustering algorithm to group the documents using a similarity metric that is based on keyword matching and one that uses the probabilistic graph. We show that the second approach produces higher precision and recall, which corresponds to better alignment with the classification that was done by human experts.


Computer Sciences

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URL: https://digitalcommons.calpoly.edu/csse_fac/264