I guess your problem is a form of NER (Named-Entity Recognition) tagging. NER tags consist of PER(person), LOC(location), ORG(organization) and MISC(miscellaneous) and O(other). with the help of a NER-tagger algorithm, you probably would have:
My(O) favorite(O) book(O) is(O) harry(B-MISC) potter(I-MISC) and(I-MISC) the(I-MISC) prisoner(I-MISC) of(I-MISC) azkaban(I-MISC).
now you can tokenize your text with identifying different NER tags, and join "B-" and "I-" prefixes with the same NER tag. e.g. you have "harry potter and ..." as a single token, which is a MISC and starts from harry (since harry is B-MISC) and ends to azkaban (since azkaban has the last consecutive I-MISC tag). Now you can let your POS tagger act the "harry potter ..." as a single token, and it must tag it with "NN"
Another solution for this problem is called "chunking". It works based on a set of rules and detect Noun Phrases (NPs). Here, you define a rule and create the regexp statement for it. e.g. you define all consecutive
NNs as a single NP (harry potter):
<NN>+ or a
DT followed by a string of
NNs (the brown fox):
<DT>?<NN>+. Now you tokenize your text based on a regexp matching method. but in your case, chunking such a long NP is almost impossible. because a rule that finds this NP as a NP candidate, would by mistake find a lot of other string of words as a NP candidate too, while they're not a NP at all. (see nltk: information extraction)