Error Analysis for Learning-based Coreference Resolution

Olga Uryupina


Abstract
State-of-the-art coreference resolution engines show similar performance figures (low sixties on the MUC-7 data). Our system with a rich linguistically motivated feature set yields significantly better performance values for a variety of machine learners, but still leaves substantial room for improvement. In this paper we address a relatively unexplored area of coreference resolution - we present a detailed error analysis in order to understand the issues raised by corpus-based approaches to coreference resolution.
Anthology ID:
L08-1049
Volume:
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)
Month:
May
Year:
2008
Address:
Marrakech, Morocco
Editors:
Nicoletta Calzolari, Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2008/pdf/487_paper.pdf
DOI:
Bibkey:
Cite (ACL):
Olga Uryupina. 2008. Error Analysis for Learning-based Coreference Resolution. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
Cite (Informal):
Error Analysis for Learning-based Coreference Resolution (Uryupina, LREC 2008)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2008/pdf/487_paper.pdf