Coreference Resolution with and without Linguistic Knowledge

Olga Uryupina


Abstract
State-of-the-art statistical approaches to the Coreference Resolution task rely on sophisticated modeling, but very few (10-20) simple features. In this paper we propose to extend the standard feature set substantially, incorporating more linguistic knowledge. To investigate the usability of linguistically motivated features, we evaluate our system for a variety of machine learners on the standard dataset (MUC-7) with the traditional learning set-up.
Anthology ID:
L06-1453
Volume:
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
Month:
May
Year:
2006
Address:
Genoa, Italy
Editors:
Nicoletta Calzolari, Khalid Choukri, Aldo Gangemi, Bente Maegaard, Joseph Mariani, Jan Odijk, Daniel Tapias
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2006/pdf/726_pdf.pdf
DOI:
Bibkey:
Cite (ACL):
Olga Uryupina. 2006. Coreference Resolution with and without Linguistic Knowledge. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).
Cite (Informal):
Coreference Resolution with and without Linguistic Knowledge (Uryupina, LREC 2006)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2006/pdf/726_pdf.pdf