Summary of the paper
| Title | Using Large Biomedical Databases as Gold Annotations for Automatic Relation Extraction |
|---|---|
| Authors | Tilia Ellendorff, Fabio Rinaldi and Simon Clematide |
| 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. |
| Topics | Text Mining, Metadata |
Full paper  |
Using Large Biomedical Databases as Gold Annotations for Automatic Relation Extraction |
| Bibtex | @InProceedings{ELLENDORFF14.1156, author = {Tilia Ellendorff and Fabio Rinaldi and Simon Clematide}, title = {Using Large Biomedical Databases as Gold Annotations for Automatic Relation Extraction}, booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)}, year = {2014}, month = {may}, date = {26-31}, address = {Reykjavik, Iceland}, editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis}, publisher = {European Language Resources Association (ELRA)}, isbn = {978-2-9517408-8-4}, language = {english} } |