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NLU-Co at SemEval-2020 Task 5: NLU/SVM based model apply to characterise and extract counterfactual items on raw data

Abstract : In this article, we try to solve the problem of classification of counterfactual statements and extraction of antecedents/consequences in raw data, by mobilizing on one hand Support vector machine (SVMs) and on the other hand Natural Language Understanding (NLU) infrastructures available on the market for conversational agents. Our experiments allowed us to test different pipelines of two known platforms (Snips NLU and Rasa NLU). The results obtained show that a Rasa NLU pipeline, built with a well-preprocessed dataset and tuned algorithms, allows to model accurately the structure of a counterfactual event, in order to facilitate the identification and the extraction of its components.
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https://hal.archives-ouvertes.fr/hal-03119450
Contributor : Elvis Mboning Tchiaze Connect in order to contact the contributor
Submitted on : Friday, February 5, 2021 - 5:04:46 PM
Last modification on : Tuesday, October 19, 2021 - 6:51:39 PM
Long-term archiving on: : Thursday, May 6, 2021 - 6:02:53 PM

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  • HAL Id : hal-03119450, version 1

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Elvis Mboning Tchiaze, Damien Nouvel. NLU-Co at SemEval-2020 Task 5: NLU/SVM based model apply to characterise and extract counterfactual items on raw data. SemEval-2020 (International Workshop on Semantic Evaluation 2020), Dec 2020, Barcelone, Spain. pp.670-676. ⟨hal-03119450⟩

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