no code implementations • 24 May 2024 • Hèctor Loopez Hidalgo, Michel Boeglin, David Kahn, Josiane Mothe, Diego Ortiz, David Panzoli
Our results show a substantial improvement in accuracy -up to a 50% improvement with our Biased PromptORE models in comparison to the baseline models using standard PromptORE.
no code implementations • 7 Feb 2024 • Adrian-Gabriel Chifu, Sébastien Déjean, Moncef Garouani, Josiane Mothe, Diégo Ortiz, Md Zia Ullah
Query performance prediction (QPP) aims to forecast the effectiveness of a search engine across a range of queries and documents.
no code implementations • 17 May 2023 • Josiane Mothe, Md Zia Ullah
To determine the ideal configurations to use on a per-query basis in real-world systems we developed a method in which a restricted number of possible configurations is pre-selected and then used in a meta-search engine that decides the best search configuration on a per query basis.
no code implementations • 22 Feb 2023 • Josiane Mothe, Md Zia Ullah
In this paper, we examine selective query processing in different settings, both in terms of effectiveness and efficiency; this includes selective query expansion and other forms of selective query processing (e. g., when the term weighting function varies or when the expansion model varies).
1 code implementation • 20 Feb 2023 • Eduard Poesina, Radu Tudor Ionescu, Josiane Mothe
To date, query performance prediction (QPP) in the context of content-based image retrieval remains a largely unexplored task, especially in the query-by-example scenario, where the query is an image.
no code implementations • 10 Sep 2021 • Md Siddiqur Rahman, Laurent Lapasset, Josiane Mothe
A controller needs to consider various types of information in order to solve a conflict.
no code implementations • 21 Dec 2020 • Ismat Ara Reshma, Sylvain Cussat-Blanc, Radu Tudor Ionescu, Hervé Luga, Josiane Mothe
The class distribution of data is one of the factors that regulates the performance of machine learning models.
no code implementations • LREC 2020 • Rami, Faneva risoa, Josiane Mothe
The evaluation part of this paper is based on the dataset provided by the TRAC shared task (Kumar et al., 2018a).
no code implementations • LREC 2020 • Rami, Faneva risoa, Josiane Mothe
This paper describes the participation of the IRIT team in the TRAC (Trolling, Aggression and Cyberbullying) 2020 shared task (Bhattacharya et al., 2020) on Aggression Identification and more precisely to the shared task in English language.
no code implementations • 30 Apr 2020 • Bernard Dousset, Josiane Mothe
We run these methodologies on two cases: the virus origin and the uses of existing drugs.
no code implementations • 4 Dec 2019 • Sébastien Déjean, Radu Tudor Ionescu, Josiane Mothe, Md Zia Ullah
We found that: (1) our model based on a limited number of selected features is as good as more complex models for QPP and better than non-selective models; (2) our model is more efficient than complex models during inference time since it requires fewer features; (3) the predictive model is readable and understandable; and (4) one of our new QPP features is consistently selected across different collections, proving its usefulness.
no code implementations • COLING 2018 • Rami, Faneva risoa, Josiane Mothe
This paper describes the participation of the IRIT team to the TRAC 2018 shared task on Aggression Identification and more precisely to the shared task in English language.
no code implementations • JEPTALNRECITAL 2018 • Josiane Mothe, Nomena Ny Hoavy, R, Mamitiana-Ignace rianarivony
Dans ce papier, nous pr{\'e}sentons une m{\'e}thode pour associer de fa{\c{c}}on automatique des concepts {\`a} des images.
no code implementations • 2 Jul 2015 • Léa Laporte, Rémi Flamary, Stephane Canu, Sébastien Déjean, Josiane Mothe
Feature selection in learning to rank has recently emerged as a crucial issue.