region:us
The dataset contains question-answer pairs to model predicate-argument structure of deverbal nominalizations. 
The questions start with wh-words (Who, What, Where, What, etc.) and contain a the verbal form of a nominalization from the sentence; 
the answers are phrases in the sentence. 
See the paper for details: QANom: Question-Answer driven SRL for Nominalizations (Klein et. al., COLING 2020)
For previewing the QANom data along with the verbal annotations of QASRL, check out "https://browse.qasrl.org/".  
This dataset was annotated by selected workers from Amazon Mechanical Turk.
@inproceedings{klein2020qanom,
  title={QANom: Question-Answer driven SRL for Nominalizations},
  author={Klein, Ayal and Mamou, Jonathan and Pyatkin, Valentina and Stepanov, Daniela and He, Hangfeng and Roth, Dan and Zettlemoyer, Luke and Dagan, Ido},
  booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
  pages={3069--3083},
  year={2020}
}
# QANom 

This dataset contains question-answer pairs to model the predicate-argument structure of deverbal nominalizations. 
The questions start with wh-words (Who, What, Where, What, etc.) and contain the verbal form of a nominalization from the sentence; 
the answers are phrases in the sentence. 

See the paper for details: [QANom: Question-Answer driven SRL for Nominalizations (Klein et. al., COLING 2020)](https://www.aclweb.org/anthology/2020.coling-main.274/)

For previewing the QANom data along with the verbal annotations of QASRL, check out https://browse.qasrl.org/. 
Also check out our [GitHub repository](https://github.com/kleinay/QANom) to find code for nominalization identification, QANom annotation, evaluation, and models. 

The dataset was annotated by selected workers from Amazon Mechanical Turk.