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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References
Blanco, E., Castell, N., Moldovan, D.: Causal Relation Extraction. In: Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC), Marrakech, Morocco (2008)
Chang, D.-S., Choi, K.-S.: Causal relation extraction using cue phrase and lexical pair probabilities. In: Su, K.-Y., Tsujii, J., Lee, J.-H., Kwong, O.Y. (eds.) IJCNLP 2004. LNCS (LNAI), vol. 3248, pp. 61–70. Springer, Heidelberg (2005)
Hendrickx, I., Kim, S.N., Kozareva, Z., Nakov, P., Saghdha, D., Romano, L., Szpakowicz, S.: Semeval-2010 task 8: Multi-way classification of semantic relations between pairs of nominal
Sorgente, A., Vettigli, G., Mele, F.: Automatic Extraction of Cause-Effect Relations in Natural Language Text. DART@AI*IA 2013, pp. 37–48 (2013)
Girju, R., Moldovan, D.: Mining answers for causation questions. In: Symposium on Mining Answers from Texts and Knowledge Bases (2002)
Kipper-Schuler, K.: VerbNet. A broad coverage, comprehensive verb lexicon. Ph.D. thesis, University of Pennsylvania, Philadelphia, PA (2005)
Pal, S., Pakray, P., Das, D., Bandyopadhyay, S.: A Supervised Approach to Identify Semantic Relations from Paired Nominals. In: ACL-2010, SemEval 2010 Workshop, Uppsala, Sweden (2010)
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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
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