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.
Mots clés
Word Embeddings, Information Retrieval, Queries, Search Strategies
Word Embeddings Information Retrieval Queries Search Strategies
Word Embeddings
Information Retrieval
Queries
Search Strategies
word embeddings information retrieval queries search strategies
word embeddings
information retrieval
queries
search strategies
Origine : Fichiers produits par l'(les) auteur(s)