Project description
Searching for better search engine systems
Search engines are prime examples of information retrieval (IR) systems. Today’s IR systems use machine learning (ML) and deep learning (DL) models. Searches are based on full text or other content-based indexing. As such, IR systems rank item by relevance (semantic similarity between the user query and the information conveying items). The EU-funded METER project will extend IR evaluation measures to deal with multiple aspects. By integrating these new measures into ML algorithms, the project will develop multi-aspect IR systems. The project will also analyse IR evaluation measures to make use of differentiability in the form of properties, in order to improve the search of local minima for IR loss functions. The findings of will boost our understanding of how search engines work.
Objective
Information Retrieval (IR) deals with the automatic retrieval and ranking of information conveying items, which are relevant to a specific information need, from a large collection of items. Search engines are the most popular and well known examples of IR systems.
State-of-the-art IR systems use sophisticated Machine Learning (ML) and Deep Learning (DL) models. Those models usually minimize a loss function which is built upon an IR evaluation measure, i.e. a measure that evaluates the quality of a ranked list of items.
This project, Multi-aspect and diffErenTiable Evaluation of Rankings (METER), will tackle two open challenges for state-of-the-art IR systems. First, traditionally IR systems ranks items only by relevance, estimated as the semantic similarity between the user query and the information conveying items. However, beside relevance, understandability and trustworthines are fundamental for health search, or credibility and correctness should be considered for news search. Therefore, the first goal of METER will be to extend IR evaluation measures to deal with mutiple aspects. Then, these new evaluation measures will be integrated in ML algorithms, to develop multi-aspect IR systems.
Second, IR measures are non-continuous and non-differentiable. This represents an issue for ML algorithms, which usually exploit gradient based approaches to minize the loss function. Therefore, the second goal of METER will be to thoroughly analyze IR evaluation measures and propose differentiability like properties which will help for the search of minima of the loss function.
Therefore, METER has the potential for making both a scientific and a societal impact: 1) multi-aspect measures will be used to account for several aspect and improve the effectiveness of IR systems in different domains; 2) differentiability like properties will be exploited to improve the search of local minima for IR loss functions and better understand how this search is performed.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
See all projects funded under this programme -
H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2019
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
1165 KOBENHAVN
Denmark
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.