Abstract
The objective of the present work is to automatically extract the cause and effect from discourse analyzed biomedical corpus. Cause-effect is defined as a relation established between two events, where first event acts as the cause of second event and the second event is the effect of first event. Any causative constructions need three components, a causal marker, cause and effect. In this study, we consider the automatic extraction of cause and effect realized by explicit discourse connective markers. We evaluated our system using BIONLP/NLPBA 2004 shared task test data and obtained encouraging results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
