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Erschienen in:
2014 | OriginalPaper | Buchkapitel
An Open-Domain Cause-Effect Relation Detection from Paired Nominals
verfasst von : Partha Pakray, Alexander Gelbukh
Erschienen in: Nature-Inspired Computation and Machine Learning
Verlag: Springer International Publishing
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We present a supervised method for detecting causal relations from text. Various kinds of dependency relations, WordNet features, Parts-of-Speech (POS) features along with several combinations of these features help to improve the performance of our system. In our experiments, we used SemEval-2010 Task #8 data sets. This system used 7954 instances for training and 2707 instances for testing from Task #8 datasets. The J48 algorithm was used to identify semantic causal relations in a pair of nominals. Evaluation result gives an overall F1 score of 85.8% of causal instances.