I read the paper on Local Context Analysis (LCA) by Xiu and Croft and found that they have done a topic level analysis at the document level i.e. they have tried to categorize the pseudo relevant documents into several topics and then have chosen the query terms from the topic which is most likely to be relevant to the given query.

It struck me that if I could possibly do some sub-topic categorization and select expansion terms only from the most relevant sub-topic then we might be getting better results than conventional feedback where query terms are chosen from anywhere in the document. This focussed scheme of choosing the expansion terms intuitively should give better results. I ran some experiments on FIRE data and found out that this is indeed true.

Now, I have to run some experiments on Bengali topic sets.