Prediction of Missing Semantic Relations in Lexical-Semantic Network using Random Forest Classifier

Kévin Cousot 1 Mehdi Mirzapour 1 Waleed Ragheb 2
1 TEXTE - Exploration et exploitation de données textuelles
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : This study focuses on the prediction of missing six semantic relations (such as is_a and has_part) between two given nodes in RezoJDM a French lexical-semantic network. The output of this prediction is a set of pairs in which the first entries are semantic relations and the second entries are the probabilities of existence of such relations. Due to the statement of the problem we choose the random forest (RF) predictor classifier approach to tackle this problem. We take for granted the existing semantic relations, for training/test dataset, gathered and validated by crowdsourcing. We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase. We show how this approach can lead to acceptable results.
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Submitted on : Friday, November 8, 2019 - 8:05:35 PM
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  • HAL Id : hal-02356649, version 1
  • ARXIV : 1911.04759

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Kévin Cousot, Mehdi Mirzapour, Waleed Ragheb. Prediction of Missing Semantic Relations in Lexical-Semantic Network using Random Forest Classifier. CJC PRAXILING 2019, Nov 2019, Montpellier, France. ⟨hal-02356649⟩

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