Ricardo Pietrobon


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A Corpus of Scientific Biomedical Texts Spanning over 168 Years Annotated for Uncertainty
Ramona Bongelli | Carla Canestrari | Ilaria Riccioni | Andrzej Zuczkowski | Cinzia Buldorini | Ricardo Pietrobon | Alberto Lavelli | Bernardo Magnini
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Uncertainty language permeates biomedical research and is fundamental for the computer interpretation of unstructured text. And yet, a coherent, cognitive-based theory to interpret Uncertainty language and guide Natural Language Processing is, to our knowledge, non-existing. The aim of our project was therefore to detect and annotate Uncertainty markers ― which play a significant role in building knowledge or beliefs in readers' minds ― in a biomedical research corpus. Our corpus includes 80 manually annotated articles from the British Medical Journal randomly sampled from a 168-year period. Uncertainty markers have been classified according to a theoretical framework based on a combined linguistic and cognitive theory. The corpus was manually annotated according to such principles. We performed preliminary experiments to assess the manually annotated corpus and establish a baseline for the automatic detection of Uncertainty markers. The results of the experiments show that most of the Uncertainty markers can be recognized with good accuracy.


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Extracting Formulaic and Free Text Clinical Research Articles Metadata using Conditional Random Fields
Sein Lin | Jun-Ping Ng | Shreyasee Pradhan | Jatin Shah | Ricardo Pietrobon | Min-Yen Kan
Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents