{"_id":"J7o3QXZJA7Wvq6zT3","bibbaseid":"cole-royal-valtorta-huhns-bowles-alightweighttoolforautomaticallyextractingcausalrelationshipsfromtext-2006","authorIDs":[],"author_short":["Cole, S., V.","Royal, M., D.","Valtorta, M., G.","Huhns, M., N.","Bowles, J., B."],"bibdata":{"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","file_attached":false,"profile_id":"5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6","group_id":"066b42c8-f712-3fc3-abb2-225c158d2704","last_modified":"2017-03-14T14:36:19.698Z","tags":"subject-verb-object","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"Cole2006","private_publication":false,"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","bibtex":"@inProceedings{\n title = {A Lightweight Tool for Automatically Extracting Causal Relationships from Text},\n type = {inProceedings},\n year = {2006},\n identifiers = {[object Object]},\n pages = {125-129},\n websites = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1629336},\n publisher = {Ieee},\n id = {3ceda746-5279-3f97-b855-07f8277aab9c},\n created = {2011-01-11T04:17:40.000Z},\n file_attached = {false},\n profile_id = {5284e6aa-156c-3ce5-bc0e-b80cf09f3ef6},\n group_id = {066b42c8-f712-3fc3-abb2-225c158d2704},\n last_modified = {2017-03-14T14:36:19.698Z},\n tags = {subject-verb-object},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {Cole2006},\n private_publication = {false},\n 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},\n bibtype = {inProceedings},\n author = {Cole, Stephen V and Royal, Matthew D and Valtorta, Marco G and Huhns, Michael N and Bowles, John B},\n booktitle = {SoutheastCon 2006 Proceedings of the IEEE}\n}","author_short":["Cole, S., V.","Royal, M., D.","Valtorta, M., G.","Huhns, M., N.","Bowles, J., B."],"urls":{"Website":"http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=1629336"},"bibbaseid":"cole-royal-valtorta-huhns-bowles-alightweighttoolforautomaticallyextractingcausalrelationshipsfromtext-2006","role":"author","downloads":0,"html":""},"bibtype":"inProceedings","creationDate":"2020-02-06T23:48:11.688Z","downloads":0,"keywords":[],"search_terms":["lightweight","tool","automatically","extracting","causal","relationships","text","cole","royal","valtorta","huhns","bowles"],"title":"A Lightweight Tool for Automatically Extracting Causal Relationships from Text","year":2006}