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
Venues:
CRAC | NAACL | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
44–49
Language:
URL:
https://www.aclweb.org/anthology/W18-0705
DOI:
10.18653/v1/W18-0705
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0705.pdf