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

Claire Ibarboure, Ludovic Tanguy, Franck Amadieu

2023

Abstract

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 (e.g. 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.

Paper Citation

in Harvard Style

Ibarboure C., Tanguy L. and Amadieu F. (2023). Which Word Embeddings for Modeling Web Search Queries? Application to the Study of Search Strategies. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 273-280. DOI: 10.5220/0012177600003598

in Bibtex Style

@conference{kdir23,
author={Claire Ibarboure and Ludovic Tanguy and Franck Amadieu},
title={Which Word Embeddings for Modeling Web Search Queries? Application to the Study of Search Strategies},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={273-280},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012177600003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - Which Word Embeddings for Modeling Web Search Queries? Application to the Study of Search Strategies
SN - 978-989-758-671-2
AU - Ibarboure C.
AU - Tanguy L.
AU - Amadieu F.
PY - 2023
SP - 273
EP - 280
DO - 10.5220/0012177600003598
PB - SciTePress