TY - GEN
T1 - Detecting Causally Embedded Structures Using an Evolutionary Algorithm
AU - Li, Chen
AU - Girju, C. Roxana
N1 - Publisher Copyright:
© 2015 Association for Computational Linguistics
PY - 2015
Y1 - 2015
N2 - Causality is an important relation among events and entities. Embedded causal structures represent an important class, expressing complex causal chains; but they are traditionally difficult to uncover automatically. In this paper we propose a method for the efficient identification and extraction of embedded causal relations with minimal supervision, by combining a representation of structured language data with modified prototype theory specifically suited to the data type. We then utilize a form of genetic algorithm specifically adapted for our purpose to locate the likely candidate linguistic structures that contain causal chains. With this procedure, we were able to identify many embedded structures with complex causal chains in two corpora of different genres, applying this algorithm as a ranking procedure for all structures in the data. We obtained 79.5% percision for top quantiles of both of our datasets (BNC & novels).
AB - Causality is an important relation among events and entities. Embedded causal structures represent an important class, expressing complex causal chains; but they are traditionally difficult to uncover automatically. In this paper we propose a method for the efficient identification and extraction of embedded causal relations with minimal supervision, by combining a representation of structured language data with modified prototype theory specifically suited to the data type. We then utilize a form of genetic algorithm specifically adapted for our purpose to locate the likely candidate linguistic structures that contain causal chains. With this procedure, we were able to identify many embedded structures with complex causal chains in two corpora of different genres, applying this algorithm as a ranking procedure for all structures in the data. We obtained 79.5% percision for top quantiles of both of our datasets (BNC & novels).
UR - http://www.scopus.com/inward/record.url?scp=85123643961&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:85123643961
T3 - NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation, EVENTS 2015
SP - 43
EP - 52
BT - NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 3rd Workshop on EVENTS: Definition, Detection, Coreference, and Representation, EVENTS 2015 at the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2015
Y2 - 4 June 2015
ER -