, modèle neuronal pour la prédiction sémantique de l'emplacement de tweets et de POIs est généralement beaucoup moins efficace que l'apprentissage d'une fonction d'appariement sémantique spatial comme le fait le modèle SGM. Plus spécifiquement, nous pouvons remarquer, les modèles SBERT mis à part, que l'amélioration de la MRR varie entre 96

, environ 22% (resp. 19%) des tweets sont correctement associés à leur POI correspondant avec le modèle Word2Vec, contre environ 10% (resp. 9%) avec le modèle Dis-tillBert. Nos résultats corroborent donc les conclusions de Reimers et Gurevych (2019) ; (2) : l'ajustement des représentations distribuées du modèle BERT à l'aide d'une tâche de similarité sémantique (c.-à-d. les modèles SBERT STS , SBERT NY et SBERT SG ) permet d'améliorer les performances de prédiction. Cette augmentation est faible lorsque la collection utilisée pour l'étape d'ajustement est différente de la collection de test (c.-à-d. SBERT STS ), mais elle est significative et permet d'obtenir des résultats comparables au modèle SGM lorsque la collection utilisée pour l'étape d'ajustement est identique à la collection de test (c.-à-d. SBERT X ). En effet, la MRR augmente de 0, 300 à 0, 712 (resp. de 0, 282 à 0, 721) pour le jeu de données de NY (resp. SG). Par ailleurs, nous notons que le modèle SBERT NY réalise une performance légèrement plus élevée que le modèle SGM sur le jeu de données NY, En ce qui concerne les modèles s'appuyant sur l'architecture de BERT, nous constatons ce qui suit : (1) l'utilisation des plongements lexicaux DistillBert sans ajustements réalise de moins bonnes performances que les plongements lexicaux traditionnels Word2Vec. Plus précisément, pour le jeu de données de NY (resp. SG)

, Analyse qualitative d'un échantillon de tweets Nous effectuons maintenant une analyse qualitative au niveau des tweets, pour déterminer les raisons du succès ou de l'échec du modèle SGM par rapport aux modèles de référence. Nous choisissons, dans chaque catégorie définie dans la Section 4.3, les modèles qui ont donné les meilleurs résultats en terme d'Acc@1, à savoir Dist, BM25 et SBERT X . Nous commençons par identifier les ensembles de tweets pour lesquels notre modèle SGM a fait moins bien (T ? ), à obtenu le même résultat (T = ), ou a fait mieux (T + ) que les modèles sélectionnés. Les résultats

D. Tableau, nous pouvons remarquer que, par rapport au modèle Dist, le modèle SGM améliore la qualité d'appariement de près de la moitié des tweets pour les deux jeux de données. Plus précisément, 49, 60% (resp. 44, 40%) des tweets sont mieux associés à leur POI avec le modèle SGM qu

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