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
- 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:
- 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)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2008/pdf/487_paper.pdf