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} } |