I have a question about classifying documents using supervised learning and unsupervised learning.
For example: - I have a bunch of documents talking about football.
As we know football has different meaning in UK, USA and Australia. Therefore, it is difficult to classify these documents to three different categorizations which are soccer, American football and Australian football.
My approach tries to use cosine similarity terms which is based on unsupervised. After we use the cluster learning, we are able to create a number of clusters based on cosine similarity which each cluster will contain similar documents terms. After we create the clusters, we can use a semantic feature to identify these clusters depend on supervised model like SVM to make accurate categorizations.
My goal is to create more accurate categorizations because if I want to test a new document I want know if this document can be related to these categorizations or not.