Challenges in Pronoun Resolution System for Biomedical Text

Ngan Nguyen, Jin-Dong Kim, Jun’ichi Tsujii


Abstract
This paper presents our findings on the feasibility of doing pronoun resolution for biomedical texts, in comparison with conducting pronoun resolution for the newswire domain. In our experiments, we built a simple machine learning-based pronoun resolution system, and evaluated the system on three different corpora: MUC, ACE, and GENIA. Comparative statistics not only reveal the noticeable issues in constructing an effective pronoun resolution system for a new domain, but also provides a comprehensive view of those corpora often used for this task.
Anthology ID:
L08-1071
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/607_paper.pdf
DOI:
Bibkey:
Cite (ACL):
Ngan Nguyen, Jin-Dong Kim, and Jun’ichi Tsujii. 2008. Challenges in Pronoun Resolution System for Biomedical Text. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).
Cite (Informal):
Challenges in Pronoun Resolution System for Biomedical Text (Nguyen et al., LREC 2008)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2008/pdf/607_paper.pdf