We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Is it possible to predict multi-word expression (MWE) from raw text? I run predict.py with option --raw_text to find that MWE cannot be predicted.
predict.py
--raw_text
For example, in Italy, "della" is abbreviation of "di la" and UD annotates such token like as follows:
31-32 della _ _ _ _ _ _ _ _ 31 di di ADP E _ 35 case 35:case _ 32 la il DET RD Definite=Def|Gender=Fem|Number=Sing|PronType=Art 35 det 35:det _
However, the output of UDify is something like this:
31 della della ADP _ _ 3 case _ _
I hope to obtain the conllu output with proper MWE. Are there any way to realize it?
The text was updated successfully, but these errors were encountered:
No branches or pull requests
Is it possible to predict multi-word expression (MWE) from raw text?
I run
predict.py
with option--raw_text
to find that MWE cannot be predicted.For example, in Italy, "della" is abbreviation of "di la" and UD annotates such token like as follows:
However, the output of UDify is something like this:
I hope to obtain the conllu output with proper MWE. Are there any way to realize it?
The text was updated successfully, but these errors were encountered: