Abstract
We show how to use large biomedical databases in order to obtain a gold standard for training a machine learning system over a corpus of biomedical text. As an example we use the Comparative Toxicogenomics Database (CTD) and describe by means of a short case study how the obtained data can be applied. We explain how we exploit the structure of the database for compiling training material and a testset. Using a Naive Bayes document classification approach based on words, stem bigrams and MeSH descriptors we achieve a macro-average F-score of 61% on a subset of 8 action terms. This outperforms a baseline system based on a lookup of stemmed keywords by more than 20%. Furthermore, we present directions of future work, taking the described system as a vantage point. Future work will be aiming towards a weakly supervised system capable of discovering complete biomedical interactions and events.- Anthology ID:
- L14-1110
- Volume:
- Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)
- Month:
- May
- Year:
- 2014
- Address:
- Reykjavik, Iceland
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Hrafn Loftsson, Bente Maegaard, Joseph Mariani, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 3736–3741
- Language:
- URL:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/1156_Paper.pdf
- DOI:
- Cite (ACL):
- Tilia Ellendorff, Fabio Rinaldi, and Simon Clematide. 2014. Using Large Biomedical Databases as Gold Annotations for Automatic Relation Extraction. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3736–3741, Reykjavik, Iceland. European Language Resources Association (ELRA).
- Cite (Informal):
- Using Large Biomedical Databases as Gold Annotations for Automatic Relation Extraction (Ellendorff et al., LREC 2014)
- PDF:
- http://www.lrec-conf.org/proceedings/lrec2014/pdf/1156_Paper.pdf