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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?

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