Abstract
We present two systems for bridging resolution, which we submitted to the CRAC shared task on bridging anaphora resolution in the ARRAU corpus (track 2): a rule-based approach following Hou et al. 2014 and a learning-based approach. The re-implementation of Hou et al. 2014 achieves very poor performance when being applied to ARRAU. We found that the reasons for this lie in the different bridging annotations: whereas the rule-based system suggests many referential bridging pairs, ARRAU contains mostly lexical bridging. We describe the differences between these two types of bridging and adapt the rule-based approach to be able to handle lexical bridging. The modified rule-based approach achieves reasonable performance on all (sub)-tasks and outperforms a simple learning-based approach.- Anthology ID:
- W18-0703
- Volume:
- Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Venue:
- CRAC
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 23–33
- Language:
- URL:
- https://aclanthology.org/W18-0703
- DOI:
- 10.18653/v1/W18-0703
- Cite (ACL):
- Ina Roesiger. 2018. Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus. In Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference, pages 23–33, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus (Roesiger, CRAC 2018)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W18-0703.pdf