Regarding the word sense disambiguation problem, read the following fact written on Knowledge-based Word Sense Disambiguation using Topic Models:
Note that although WordNet is the most widely used sense repository, the sense distinctions can be too ﬁne-grained in many scenarios. This makes it difﬁcult for expert annotators to agree on a correct sense, resulting in a very low inter-annotator agreement (72%) in standard WSD datasets.
I would like to understand better the process of assignment of a sense to a word but there is no detailed explanation at the web.
Can somebody here give some examples of cases which are hard to find a sense using WordNet?