I have done lots of reading around Linear CRF and Word2Vec and wanted to know which one is the best to do Named Entity Recognition. I trained my model using Stanford NER(Which is a Linear CRF Implementation) and got a precision of 85%. I know that Word2vec groups similar words together but is it a good model to do NER?
CRFs and word2vec are apples and oranges, so comparing them doesn't really make sense.
CRFs are used for sequence labelling problems like NER. Given a sequence of items, represented as features and paired with labels, they'll learn a model to predict labels for new sequences.
Word2vec's word embeddings are representations of words as vectors of floating point numbers. They don't predict anything by themselves. You can even use the word vectors to build features in a CRF, though it's more typical to use them with a neural model like an LSTM.
Some people have used word vectors with CRFs with success. For some discussion of using word vectors in a CRF see here and here.
Do note that with many standard CRF implementations features are expected to be binary or categorical, not continuous, so you typically can't just shove word vectors in as you would another feature.
If you want to know which is better for your use case, the only way to find out is to try both.