Unsupervised feature learning for NER
Yes, it is entirely possible to combine unsupervised learning with the CRF model.  In particular, I would recommend that you explore the possibility of using word2vec features as inputs to your CRF.

Word2vec trains a  to distinguish between words that are appropriate for a given context and words that are randomly selected.  Select weights of the model can then be interpreted as a dense vector representation of a given word.  

These dense vectors have the appealing property that words that are semantically or syntactically similar have similar vector representations.  Basic vector arithmetic even reveals some interesting learned relationships between words.
For example, vector("Paris") - vector("France") + vector("Italy") yields a vector that is quite similar to vector("Rome").  

At a high level, you can think of word2vec representations as being similar to LDA or LSA representations, in the sense that you can convert a sparse input vector into a dense output vector that contains word similarity information. 

For that matter, LDA and LSA are also valid options for unsupervised feature learning -- both attempt to represent words as combinations of "topics" and output dense word representations.  

For English text Google distributes word2vec models pretrained on a huge 100 billion word Google News dataset, but for other languages you'll have to train your own model.