Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution
Ina Roesiger, Maximilian Köper, Kim Anh Nguyen, Sabine Schulte im Walde
Abstract
Cases of coreference and bridging resolution often require knowledge about semantic relations between anaphors and antecedents. We suggest state-of-the-art neural-network classifiers trained on relation benchmarks to predict and integrate likelihoods for relations. Two experiments with representations differing in noise and complexity improve our bridging but not our coreference resolver.- Anthology ID:
- W18-0705
- Volume:
- Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Massimo Poesio, Vincent Ng, Maciej Ogrodniczuk
- Venue:
- CRAC
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 44–49
- Language:
- URL:
- https://aclanthology.org/W18-0705
- DOI:
- 10.18653/v1/W18-0705
- Cite (ACL):
- Ina Roesiger, Maximilian Köper, Kim Anh Nguyen, and Sabine Schulte im Walde. 2018. Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution. In Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference, pages 44–49, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution (Roesiger et al., CRAC 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W18-0705.pdf