Title |
TrigNER: automatically optimized biomedical event trigger recognition on scientific documents
|
---|---|
Published in |
Source Code for Biology and Medicine, January 2014
|
DOI | 10.1186/1751-0473-9-1 |
Pubmed ID | |
Authors |
David Campos, Quoc-Chinh Bui, Sérgio Matos, José Luís Oliveira |
Abstract |
Cellular events play a central role in the understanding of biological processes and functions, providing insight on both physiological and pathogenesis mechanisms. Automatic extraction of mentions of such events from the literature represents an important contribution to the progress of the biomedical domain, allowing faster updating of existing knowledge. The identification of trigger words indicating an event is a very important step in the event extraction pipeline, since the following task(s) rely on its output. This step presents various complex and unsolved challenges, namely the selection of informative features, the representation of the textual context, and the selection of a specific event type for a trigger word given this context. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Portugal | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 50% |
Members of the public | 1 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 1 | 2% |
Colombia | 1 | 2% |
France | 1 | 2% |
Germany | 1 | 2% |
Unknown | 41 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 8 | 18% |
Student > Master | 8 | 18% |
Student > Ph. D. Student | 6 | 13% |
Student > Bachelor | 5 | 11% |
Student > Doctoral Student | 3 | 7% |
Other | 11 | 24% |
Unknown | 4 | 9% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 21 | 47% |
Engineering | 6 | 13% |
Agricultural and Biological Sciences | 4 | 9% |
Business, Management and Accounting | 2 | 4% |
Physics and Astronomy | 2 | 4% |
Other | 4 | 9% |
Unknown | 6 | 13% |