, Pour évaluer l'efficacité du modèle, nous utilisons trois types de modèles de référence : a) Modèles d'appariement exact pour mettre en évidence l'impact de l'utilisation de la sémantique relationnelle et des approches d

?. Lm-di, Le modèle de langue basé sur le lissage de Dirichlet, qui est un autre modèle d'appariement exact, 2001.

, Modèles d'appariement sémantique pour décrire l'impact d'un modèle neuronal profond guidé par des ressources sémantiques : ? LM-QE : Un modèle de langue appliquant une technique d'expansion de requête basée sur des concepts (Pal et al., 2014) dans lequel les termes candidats sont ordonnés en fonction de leur similarité avec les descriptions des objets dans la ressource sémantique. Les paramètres par défaut mentionnés dans l'article de référence, 2014.

?. Le and L. , , 2006.

. Huang, également basé sur l'architecture siamoise, pour mettre en évidence l'impact de la combinaison de sémantique relationnelle et distributive dans les approches neuronales : ? DSSM : Le modèle d'appariement de l'état de l'art, 2013.

?. Clsm-;-shen, Nous utilisons également le code CLSM public 6 sur les documents en texte intégral avec les valeurs des paramètres par défaut. Pour comparer la performance de notre modèle DSRIM sur différents types de vecteur d'entrée, nous utilisons les trois configurations suivantes : ? DSRI M p2v : Notre modèle neuronal basé sur une représentation d, L'extension du modèle DSSM dont l'entrée est remplacé par un réseau de convolution pour mieux capturer des structures contextuelles détaillées, 2014.

?. Dsri-m-kr, Notre modèle neuronal basé sur notre représentation symbolique du texte, à savoir x KR

?. Dsri-m-kr+p2v, Notre modèle neuronal basé sur une représentation améliorée des textes combinant la représentation de texte brut x t et notre représentation symbolique x KR

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