Summary of the paper

Title Challenges in Pronoun Resolution System for Biomedical Text
Authors Ngan Nguyen, Jin-Dong Kim and 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.
Language
Topics Anaphora, Coreference, Corpus (creation, annotation, etc.), Acquisition, Machine Learning
Full paper Challenges in Pronoun Resolution System for Biomedical Text
Slides Challenges in Pronoun Resolution System for Biomedical Text
Bibtex @InProceedings{NGUYEN08.607,
  author = {Ngan Nguyen, Jin-Dong Kim and Jun’ichi Tsujii},
  title = {Challenges in Pronoun Resolution System for Biomedical Text},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  month = {may},
  date = {28-30},
  address = {Marrakech, Morocco},
  editor = {Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Daniel Tapias},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {2-9517408-4-0},
  note = {http://www.lrec-conf.org/proceedings/lrec2008/},
  language = {english}
  }

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