We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom. This scheme extends the QA-SRL formalism (He et al., 2015), modeling the relations between nominalizations and their arguments via natural language question-answer pairs. We construct the first QANom dataset using controlled crowdsourcing, analyze its quality and compare it to expertly annotated nominal-SRL annotations, as well as to other QA-driven annotations. In addition, we train a baseline QANom parser for identifying nominalizations and labeling their arguments with question-answer pairs. Finally, we demonstrate the extrinsic utility of our annotations for downstream tasks using both indirect supervision and zero-shot settings.
|Name||COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference|
|Conference||28th International Conference on Computational Linguistics, COLING 2020|
|Period||8/12/20 → 13/12/20|
- Theoretical Computer Science
- Computer Science Applications
- Computational Theory and Mathematics