An open-domain cause-effect relation detection from paired nominals

Partha Pakray; Alexander Gelbukh

doi:10.1007/978-3-319-13650-9_24

An open-domain cause-effect relation detection from paired nominals

Partha Pakray, Alexander Gelbukh

Centro de Investigación en Computación (CIC)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

13 Scopus citations

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.

Original language	English
Title of host publication	Nature-Inspired Computation and Machine Learning - 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Proceedings
Editors	Alexander Gelbukh, Félix A. Castro-Espinoza, Sofía N. Galicia-Haro
Publisher	Springer Verlag
Pages	263-271
Number of pages	9
ISBN (Electronic)	9783319136493
DOIs	https://doi.org/10.1007/978-3-319-13650-9_24
State	Published - 2014
Event	13th Mexican International Conference on Artificial Intelligence, MICAI 2014 - Tuxtla Gutiérrez, Mexico Duration: 16 Nov 2014 → 22 Nov 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8857
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	13th Mexican International Conference on Artificial Intelligence, MICAI 2014
Country/Territory	Mexico
City	Tuxtla Gutiérrez
Period	16/11/14 → 22/11/14

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10.1007/978-3-319-13650-9_24

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Pakray, P., & Gelbukh, A. (2014). An open-domain cause-effect relation detection from paired nominals. In A. Gelbukh, F. A. Castro-Espinoza, & S. N. Galicia-Haro (Eds.), Nature-Inspired Computation and Machine Learning - 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Proceedings (pp. 263-271). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8857). Springer Verlag. https://doi.org/10.1007/978-3-319-13650-9_24

Pakray, Partha ; Gelbukh, Alexander. / An open-domain cause-effect relation detection from paired nominals. Nature-Inspired Computation and Machine Learning - 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Proceedings. editor / Alexander Gelbukh ; Félix A. Castro-Espinoza ; Sofía N. Galicia-Haro. Springer Verlag, 2014. pp. 263-271 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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 A Gelbukh, FA Castro-Espinoza & SN Galicia-Haro (eds), Nature-Inspired Computation and Machine Learning - 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8857, Springer Verlag, pp. 263-271, 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Tuxtla Gutiérrez, Mexico, 16/11/14. https://doi.org/10.1007/978-3-319-13650-9_24

An open-domain cause-effect relation detection from paired nominals. / Pakray, Partha; Gelbukh, Alexander.
Nature-Inspired Computation and Machine Learning - 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Proceedings. ed. / Alexander Gelbukh; Félix A. Castro-Espinoza; Sofía N. Galicia-Haro. Springer Verlag, 2014. p. 263-271 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8857).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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.

AB - 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. An open-domain cause-effect relation detection from paired nominals. In Gelbukh A, Castro-Espinoza FA, Galicia-Haro SN, editors, Nature-Inspired Computation and Machine Learning - 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, Proceedings. Springer Verlag. 2014. p. 263-271. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-13650-9_24

An open-domain cause-effect relation detection from paired nominals

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