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).
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