The Meta-knowledge of Causality in Biomedical Scientific Discourse

Claudiu Mihăilă, Sophia Ananiadou

Abstract
Causality lies at the heart of biomedical knowledge, being involved in diagnosis, pathology or systems biology. Thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. For this, we rely on corpora that are annotated with classified, structured representations of important facts and findings contained within text. However, it is impossible to correctly interpret these annotations without additional information, e.g., classification of an event as fact, hypothesis, experimental result or analysis of results, confidence of authors about the validity of their analyses etc. In this study, we analyse and automatically detect this type of information, collectively termed meta-knowledge (MK), in the context of existing discourse causality annotations. Our effort proves the feasibility of identifying such pieces of information, without which the understanding of causal relations is limited.
Anthology ID:
L14-1221
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:
1984–1991
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/23_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Claudiu Mihăilă and Sophia Ananiadou. 2014. The Meta-knowledge of Causality in Biomedical Scientific Discourse. In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 1984–1991, Reykjavik, Iceland. European Language Resources Association (ELRA).
Cite (Informal):
The Meta-knowledge of Causality in Biomedical Scientific Discourse (Mihăilă & Ananiadou, LREC 2014)
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PDF:
http://www.lrec-conf.org/proceedings/lrec2014/pdf/23_Paper.pdf
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