We experiment with both full synsets and SFs as instances of fine-grained and coarse-grained semantic representation, respectively.

The more fine-grained our semantic representation, the higher the average polysemy and the greater the need to distinguish between these senses.

Disambiguating each word relative to its context of use becomes increasingly difficult for fine-grained representations (Palmer et al., 2006).

We hypothesise that this is due to the avoidance of excessive fragmentation, as occurs with fine-grained senses.

On the one hand, their model is asymmetric, thus not giving the same interpretation power to verbs and arguments; on the other hand, the model provides a more fine-grained clustering for nouns, in the form of an additional hierarchical structure of the noun clusters.

A model with a large number of fine-grained concepts as selectional preferences assigns a higher likelihood to the data than a model with a small number of general concepts, because in general a larger number of parameters is better in describing training data.

Consequently, the EM algorithm a priori prefers fine-grained concepts but — due to sparse data problems — tends to overfit the training data.

We also tried to map the fine-grained VerbNet roles into coarser roles, but it did not yield better results than the mapping from PropBank roles.

But if we compare them to the results of the PropBank to VerbNet mapping, where we simply substitute the fine-grained roles by their corresponding groups, we see that they still lag behind (second row in Table 6).

In the case of VerbNet, the more fine-grained distinction among roles seems to depend more on the meaning of the predicate.