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Communication Dans Un Congrès COLING Année : 2020

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

Résumé

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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Dates et versions

hal-03119450 , version 1 (05-02-2021)

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  • HAL Id : hal-03119450 , version 1

Citer

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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