I'm interested in an overview of the modern/state-of-the-art approaches to bag-of-words information retrieval, where you have a single query $q$ and a set of documents which you hope to rank by relevance $d_1,...,d_n$.
I'm specifically interested in approaches which require absolutely no linguistic knowledge and rely on no outside linguistic or lexical resources for boosting performance (such as thesauri or prebuilt word-nets and the like). Thus where a ranking is produced entirely by evaluating query-document similarity and where the problems of synonymy and polysemy are overcome by exploiting inter-document word co-occurence.
I've spent some amount of time in the literature (reading papers/tutorials), however there is so much information out there it's hard to get a bird's eye view. The best I can make out is that modern approaches involve some combination of a weighted vector space model (such as a generalized vector space model, LSI, or a topic-based vector space model using LDA), in conjunction with pseudo-relevance feedback (using either Rocchio or some more advanced approach).
All these vector space models tend to use cosine-similarity as the similarity function, however I have seen some literature discussing similarity functions which are more exotic.
Is this basically where we currently are in attacking this particular type of problem?