A Lightweight Tool for Automatically Extracting Causal Relationships from Text. Cole, S., V., Royal, M., D., Valtorta, M., G., Huhns, M., N., & Bowles, J., B. In SoutheastCon 2006 Proceedings of the IEEE, pages 125-129, 2006. Ieee.
A Lightweight Tool for Automatically Extracting Causal Relationships from Text [link]Website  abstract   bibtex   
A tool that uses natural language processing techniques to extract causal relations from text and output useful Bayesian network fragments is described. Previous research indicates that a primarily syntactic approach to causal relation detection can yield good results. We used such an approach to identify subject-verb-object triples and then applied various rules to determine which of the triples were causal relations. Overall, precision and recall were low; however, causal relations with a subject-verb-object structure accounted for a low percentage of the total causal relations in the texts we analyzed. Our research shows that additional methods are needed in order to reliably detect explicit causal relations in text
@inProceedings{
 title = {A Lightweight Tool for Automatically Extracting Causal Relationships from Text},
 type = {inProceedings},
 year = {2006},
 identifiers = {[object Object]},
 pages = {125-129},
 websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1629336},
 publisher = {Ieee},
 id = {3ceda746-5279-3f97-b855-07f8277aab9c},
 created = {2011-01-11T04:17:40.000Z},
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 abstract = {A tool that uses natural language processing techniques to extract causal relations from text and output useful Bayesian network fragments is described. Previous research indicates that a primarily syntactic approach to causal relation detection can yield good results. We used such an approach to identify subject-verb-object triples and then applied various rules to determine which of the triples were causal relations. Overall, precision and recall were low; however, causal relations with a subject-verb-object structure accounted for a low percentage of the total causal relations in the texts we analyzed. Our research shows that additional methods are needed in order to reliably detect explicit causal relations in text},
 bibtype = {inProceedings},
 author = {Cole, Stephen V and Royal, Matthew D and Valtorta, Marco G and Huhns, Michael N and Bowles, John B},
 booktitle = {SoutheastCon 2006 Proceedings of the IEEE}
}
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