Title |
BioCause: Annotating and analysing causality in the biomedical domain
|
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Published in |
BMC Bioinformatics, January 2013
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DOI | 10.1186/1471-2105-14-2 |
Pubmed ID | |
Authors |
Claudiu Mihăilă, Tomoko Ohta, Sampo Pyysalo, Sophia Ananiadou |
Abstract |
Biomedical corpora annotated with event-level information represent an important resource for domain-specific information extraction (IE) systems. However, bio-event annotation alone cannot cater for all the needs of biologists. Unlike work on relation and event extraction, most of which focusses on specific events and named entities, we aim to build a comprehensive resource, covering all statements of causal association present in discourse. Causality lies at the heart of biomedical knowledge, such as diagnosis, pathology or systems biology, and, thus, automatic causality recognition can greatly reduce the human workload by suggesting possible causal connections and aiding in the curation of pathway models. A biomedical text corpus annotated with such relations is, hence, crucial for developing and evaluating biomedical text mining. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 5 | 83% |
Unknown | 1 | 17% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 83% |
Scientists | 1 | 17% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 4% |
Spain | 2 | 3% |
France | 1 | 1% |
Portugal | 1 | 1% |
United Kingdom | 1 | 1% |
Unknown | 71 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 16 | 20% |
Researcher | 15 | 19% |
Professor > Associate Professor | 8 | 10% |
Student > Bachelor | 7 | 9% |
Other | 6 | 8% |
Other | 19 | 24% |
Unknown | 8 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 33 | 42% |
Agricultural and Biological Sciences | 13 | 16% |
Linguistics | 5 | 6% |
Social Sciences | 4 | 5% |
Medicine and Dentistry | 4 | 5% |
Other | 11 | 14% |
Unknown | 9 | 11% |