Abstract
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.
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Pakray, P., Gelbukh, A. (2014). An Open-Domain Cause-Effect Relation Detection from Paired Nominals. In: Gelbukh, A., Espinoza, F.C., Galicia-Haro, S.N. (eds) Nature-Inspired Computation and Machine Learning. MICAI 2014. Lecture Notes in Computer Science(), vol 8857. Springer, Cham. https://doi.org/10.1007/978-3-319-13650-9_24
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DOI: https://doi.org/10.1007/978-3-319-13650-9_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13649-3
Online ISBN: 978-3-319-13650-9
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