Title |
Enriching a biomedical event corpus with meta-knowledge annotation
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Published in |
BMC Bioinformatics, October 2011
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DOI | 10.1186/1471-2105-12-393 |
Pubmed ID | |
Authors |
Paul Thompson, Raheel Nawaz, John McNaught, Sophia Ananiadou |
Abstract |
Biomedical papers contain rich information about entities, facts and events of biological relevance. To discover these automatically, we use text mining techniques, which rely on annotated corpora for training. In order to extract protein-protein interactions, genotype-phenotype/gene-disease associations, etc., we rely on event corpora that are annotated with classified, structured representations of important facts and findings contained within text. These provide an important resource for the training of domain-specific information extraction (IE) systems, to facilitate semantic-based searching of documents. Correct interpretation of these events is not possible without additional information, e.g., does an event describe a fact, a hypothesis, an experimental result or an analysis of results? How confident is the author about the validity of her analyses? These and other types of information, which we collectively term meta-knowledge, can be derived from the context of the event. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 29% |
Spain | 1 | 14% |
Armenia | 1 | 14% |
Unknown | 3 | 43% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 71% |
Scientists | 1 | 14% |
Unknown | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 4% |
United States | 4 | 4% |
Germany | 3 | 3% |
Australia | 1 | 1% |
Brazil | 1 | 1% |
India | 1 | 1% |
Netherlands | 1 | 1% |
Canada | 1 | 1% |
France | 1 | 1% |
Other | 2 | 2% |
Unknown | 81 | 81% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 24 | 24% |
Student > Ph. D. Student | 18 | 18% |
Student > Master | 12 | 12% |
Other | 5 | 5% |
Student > Bachelor | 5 | 5% |
Other | 21 | 21% |
Unknown | 15 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 34 | 34% |
Agricultural and Biological Sciences | 16 | 16% |
Linguistics | 7 | 7% |
Medicine and Dentistry | 4 | 4% |
Biochemistry, Genetics and Molecular Biology | 2 | 2% |
Other | 15 | 15% |
Unknown | 22 | 22% |