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I am struggling with ASySD's performance on messy datasets, e.g., combining Google Scholar, extracted references and proper databases - but given the good performance with clean datasets, I don't think it would be a good idea to start tinkering with the basic rules you developed ... but I was wondering whether they could be pulled out into a rule set that the users could then rely on as the default, but also modify as needed. If we then got a modification that performs better on messy data, that could be offered as an alternative option?
Does that sound like a worthwhile idea, @kaitlynhair (assuming that the idea is clear)? If so, I'd be happy to think about what that would mean in terms of refactoring the code ...
The text was updated successfully, but these errors were encountered:
I am struggling with ASySD's performance on messy datasets, e.g., combining Google Scholar, extracted references and proper databases - but given the good performance with clean datasets, I don't think it would be a good idea to start tinkering with the basic rules you developed ... but I was wondering whether they could be pulled out into a rule set that the users could then rely on as the default, but also modify as needed. If we then got a modification that performs better on messy data, that could be offered as an alternative option?
Does that sound like a worthwhile idea, @kaitlynhair (assuming that the idea is clear)? If so, I'd be happy to think about what that would mean in terms of refactoring the code ...
The text was updated successfully, but these errors were encountered: