In “Semantic Hashing“, Salakhutdinov and Hinton (2007) show how to classify documents with binary vectors. They combine deep learning and graphical models to assign each document a binary vector. Similar documents can be found by using the L1 difference between the binary vectors. Here is their abstract.
We show how to learn a deep graphical model of the word-count vectors obtained from a large set of documents. The values of the latent variables in the deepest layer are easy to infer and give a much better representation of each document than Latent Semantic Analysis. When the deepest layer is forced to use a small number of binary variables (e.g. 32), the graphical model performs “semantic hashing”: Documents are mapped to memory addresses in such away that semantically similar documents are located at nearby addresses. Documents similar to a query document can then be found by simply accessing all the addresses that differ by only a few bits from the address of the query document. This way of extending the efficiency of hash-coding to approximate matching is much faster than locality sensitive hashing, which is the fastest current method. By using semantic hashing to ﬁlter the documents given to TF-IDF, we achieve higher accuracy than applying TF-IDF to the entire document set.