Which Word Embeddings for Modeling Web Search Queries? Application to the Study of Search Strategies - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2023

Which Word Embeddings for Modeling Web Search Queries? Application to the Study of Search Strategies

Résumé

In order to represent the global strategies deployed by a user during an information retrieval session on the Web, we compare different pretrained vector models capable of representing the queries submitted to a search engine. More precisely, we use static (type-level) and contextual (token-level, such as provided by transformers) word embeddings on an experimental French dataset in an exploratory approach. We measure to what extent the vectors are aligned with the main topics on the one hand, and with the semantic similarity between two consecutive queries (reformulations) on the other. Even though contextual models manage to differ from the static model, it is with a small margin and a strong dependence on the parameters of the vector extraction. We propose a detailed analysis of the impact of these parameters (eg combination and choice of layers). In this way, we observe the importance of these parameters on the representation of queries. We illustrate the use of models with a representation of a search session as a trajectory in a semantic space.
Fichier principal
Vignette du fichier
Final-IbarboureTanguyAmadieu_KDIR23.pdf (172.73 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-04510852 , version 1 (19-03-2024)

Identifiants

Citer

Claire Ibarboure, Ludovic Tanguy, Franck Amadieu. Which Word Embeddings for Modeling Web Search Queries? Application to the Study of Search Strategies. 15th International Conference on Knowledge Discovery and Information Retrieval, Nov 2023, Rome, France. pp.273-280, ⟨10.5220/0012177600003598⟩. ⟨hal-04510852⟩
4 Consultations
1 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More