Thank you very much for sharing the parser. I wonder how the parse treats pb2vn mapping where pb predicate does not specific enough to identify the VN class?
I noticed that VN is more fine-grained for many cases and there seems to be critical differences in the results depending on. For instance "take.10" in prop bank can be mapped onto three VN classes "54.2", "103", or "54.3". Did you train the model with a VN specific annotated dataset to disambiguate this? OR did you manually chose one out of these three as default cases (not considering the other two?)?
If you took a statistical approach (the former), could you let me know where you got the VN annotated treebank?
If the latter is the approach, I wonder if you could share the overall strategy that you took.
I appreciate the way you handled this disambiguation task.
Thank you in advance!
Thank you very much for sharing the parser. I wonder how the parse treats pb2vn mapping where pb predicate does not specific enough to identify the VN class?
I noticed that VN is more fine-grained for many cases and there seems to be critical differences in the results depending on. For instance "take.10" in prop bank can be mapped onto three VN classes "54.2", "103", or "54.3". Did you train the model with a VN specific annotated dataset to disambiguate this? OR did you manually chose one out of these three as default cases (not considering the other two?)?
If you took a statistical approach (the former), could you let me know where you got the VN annotated treebank?
If the latter is the approach, I wonder if you could share the overall strategy that you took.
I appreciate the way you handled this disambiguation task.
Thank you in advance!